In cases where the number of features is large , feature selection is desirable to enhance model interpretability and to avoid overfitting . In this paper , we propose a robust feature selection method via l2,1-norm penalized maximum likelihood estimation ( MLE ) in FMR , with extension to sparse l2,1 penalty by combining l1-norm with l2,1-norm for increasing flexibility . To solve the non-convex and non-smooth problem of ( sparse ) penalized MLE in FMR , we develop an new EM-based algorithm for numerical optimization , with combination of block coordinate descent and majorizing-minimization scheme in M-step . Numerical simulation results indicate that the proposed approach lowers the mean square error of retrieved excitations compared to the non-convex approach using perturbation technique , with the advantage of a significant reduction of running time . In addition , it also improves the probability of success rate of diagnosis effectively compared to the l 1 norm and reweighted l 1 norm regularised methods . For M/EEG-based distributed source imaging , it has been established that the L2-norm-based methods are effective in imaging spatially extended sources , whereas the L1-norm-based methods are more suited for estimating focal and sparse sources . However , when the spatial extents of the sources are unknown a priori , the rationale for using either type of methods is not adequately supported . The median filter term makes our method eliminate additional noise from the reconstruction process and obtain much clearer reconstruction results . One key point of the proposed method lies in the fact that both the total variation term and the median filter term are presented in the L1 norm formulation . We then apply the split Bregman technique for fast minimization and give an efficient algorithm . A new nonlinear constrained L1 norm problem is constructed by taking the increment of component values and the voltage increment at inaccessible nodes in the faulty circuit for variables to be optimized . This L1 norm problem is solved based on the principle of Hopfield neural network . Using this technique , only a single process of optimization is needed to isolate the most likely faulty elements with minimal computation . It is wellknown that the l1-norm is robust to outliers ( Brooks , Dul , and Boone 2013 ; Meng , Zhao , and Xu 2012 ) . In order to alleviate this effect , Ke and Kanade ( Ke and Kanade 2005 ) proposed the robust PCA called PCA-L1 , which uses l1norm to minimize the reconstruction error . PCA-L1 achieves robust results , however , it usually gets the local optimal solution due to the discontinuity of the objective function . In this paper , we propose a robust kernel NMF approach using L2 , 1 norm loss function . Compared with the standard NMF algorithm , the new robust kernel NMF updating algorithm is as elegant and as simple , but with the newly added robustness to handle significantly corrupted datasets because of using L2 , 1 norm . Experiments on normal and occluded datasets indicate that robust kernel NMF always perform better than k-means and standard NMF . However , traditional PCA minimizes the sum of squared errors and suffers from both outliers and large feature noises . The L1-norm based PCA ( more precisely L1,1 norm ) is more robust . Yet , the optimization on L1-PCA is much harder than standard PCA . We discuss a general strategy to construct coherence measures . One can build an important class of coherence measures which cover the relative entropy measure for pure states , the l1-norm measure for pure states and the α-entropy measure . The optimal conversion of coherent states under incoherent operations is presented which sheds some light on the coherence of a single copy of a pure state . Commonly used constraints include the L1 / L2 norm and cosine similarity . We adopt the following loss function based on the L2 norm , which was originally proposed by Hadsell _ meaning : NTF . K-means algorithm in clustering is used to find the distance between the nodes or objects and the cluster centroids . The distance is calculated by either Euclidean distance , Manhattan distance or Minkowski distance . The grouping of objects is done on the basis of distance between the object and the cluster centroids(1 ) . Indian premier league dataset has been considered for the detection of clusters . Several clustering parameters like centroid , radius and Manhattan distance measure have been applied . The definition of clusters as well as the membership function has been implemented using PL/SQL . One of the most important information that an intelligent agent can have , is a representation of the environment , not necessarily in the form of a map , but of a topological graph of the plane , which can be used to perform efficient planning . This work proposes a method to produce a topological graph of an Occupancy Grid Map ( OGM ) by using a Manhattan distance function to create the Approximate Generalized Voronoi Diagram ( AGVD ) . Several improvements in the AGVD are made , in order to produce a crisp representation of the spaces skeleton , but in the same time to avoid the complex results of other methods . Four weight values are calculated for each learning object , describing how related the learner and the learning object in question is at each dimension of Felder and Silverman learning styles model . Then , weight values for each dimension is combined by using Manhattan distance metric to provide a single weight value as a fitness function representing the general relatedness of the learner and the learning object . Results of the personalization approach can be used to recommend learning objects ordered according to their weight values to the learners . A neuron MOS association processor searches for the event in the past memory having the maximum similarity to the current event presented to the system . This is based on Manhattan-distance calculation and the minimum distance search by a winner-take-all ( WTA ) circuitry . The computation is carried out directly on the hardware in a fully parallel architecture . By using the Manhattan distance concept , this study shows that the formulation of the algorithm is correct and in line with results in the literature . Hence , the Manhattan distance concept may be useful in other scheduling and optimization problems . Furthermore , this unique concept makes it possible to develop a more sustainable telecommunication system with ease of connectivity among users , even when several subscribers are on a common frequency . The color-based road detector works in the HSV color space and uses RGB normalization and white balance algorithms to reduce illuminations effects . Also , it uses a Manhattan distance measurement to segment the image into road pixels and a mean-shift algorithm to keep track of the road color characteristics . The texture line detector applies an inverse perspective mapping ( IPM ) to get rid of the perspective effects and detect parallel road borders based on a canny edge detector combined with a Hough transform . Reducing computational complexity and communication overhead is essential in order to reduce power consumption , extend the life time of the battery operated nodes , and improve the performance of the limited computational resources of these sensor nodes . To that end , we propose a novel modified Manhattan distance norm and employ it in a previous localization system so-called TALS ( Trigonometric Ad-hoc Localization System ) , such that we optimize TALS . Furthermore , an analysis and an extensive simulation for the optimized TALS ( OTALS ) is presented showing its cost , accuracy , and efficiency , thus deducing the impact of its parameters on performance . And how to maintain the image quality becomes the important issue . In this work , we propose to use a well-known video quality metric called VQM ( Video Quality Metric ) to measure the video quality , and proposed a VQM curve to be predicted by the MSE ( Mean Square Error ) . This proposed curve can later be used to efficiently maintain the video quality while performing the backlight power saving . Further , it is compared with single exponential smoothing ; double exponential smoothing with trend ; and Holt-Winter multiplicative and additive seasonal and multiple linear regression models . The performance of the models was measured by using various error functions such as root mean square error , mean absolute error , and mean absolute percentage error . The validation tests of the models were also carried out using different goodness of fit measures such as R-square , bias and tracking signal . metode Economic Order Quantity ( EOQ ) dengan dukungan perangkat PC untuk menghasilkan kinerja yang lebih optimal maka rancangan ini dibuat dengan menggunakan Software dengan bahasa pemrograman Matlab R2010a dan sistem operasi Windows 7 . K ata kunci : LPG , Mean Absolute Percentage Error , Economic Order Quantity ( EOQ ) , Matlab In embedded system , the speed of sweep fingerprint sequences stitching is very important . This paper proposes a fingerprint sequences algorithm , based on motion estimation and minimum mean absolute error . The algorithm ensures effective fingerprint stitching , reduces complexity , and improves its efficiency . The proposed FDB-HRT algorithm was 99.16 % and 99.2 % accurate with regard to heart rate measurement and fall detection , respectively . In addition , the geolocation error of patient fall incidents based on a GPS module was evaluated by mean absolute error analysis for 17 different locations in three cities in Iraq . Mean absolute error was 1.08 × 10−5 ° and 2.01 × 10−5 ° for latitude and longitude data relative to data from the GPS Benchmark system . Three measures are analyzed on a 20.2-mile long corridor in San Antonio , Texas , including corridor travel time , corridor travel-time reliability , and day-to-day variation in travel time . The primary contributions of this article are the analysis of the impact of using two different approaches for travel-time aggregation across segments — instantaneous and time-dependent approaches — and defining a mean absolute error-based method to identify days when travel times significantly deviate from typical traffic conditions . The findings suggest that the temporal patterns of corridor travel times obtained using instantaneous and time-dependent aggregation approaches are similar ; however , instantaneous travel-time estimates lead to inaccuracies that become more apparent during peak hours and for longer segments . In an effort to avoid complexities introduced by more advanced learning algorithms , the simple backpropagation with momentum algorithm has been employed . A member of classification problems were solved with neural networks that have been trained with the usual mean square error function , the mean absolute error function , the cross-entropy or maximum likelihood function , the Kalman-Kwasny error function ( 1991 ) , as well as a novel error function designed by the authors . The results indicate that , in most problems examined , an error function other than the usual mean square gives a better performance , both in terms of the number of epochs needed for training , as well as the obtained generalization ability of the trained network . The Belief Distribution Algorithm retains the qualities of nearest-neighbor algorithms which have performed well in the past , yet produces predictions of belief distributions across rating values rather than a point rating value . In addition , we illustrate how the exclusive use of the mean absolute error metric has concealed these flaws for so long , and we propose the use of a modified Precision metric for more accurately evaluating the user experience . However , N-PLS by itself does not perform variable selection , which indeed can facilitate interpretation in different situations ( e.g. the so-called “ –omics ” data ) . In this work , we propose a method for variable selection within N-PLS by introducing sparsity in the weights matrices WJ and WK by means of L1-penalization . The sparse version of N-PLS is able to provide lower prediction errors by filtering all the noise variables and to further improve interpretability and usability of the N-PLS results . The effectiveness of local search comes from the piecewise linearity of the ramp loss . Motivated by the fact that the l1-penalty is piecewise linear as well , the l1-penalty is applied for the ramp loss , resulting in a ramp loss linear programming support vector machine ( ramp-LPSVM ) . The proposed ramp-LPSVM is a piecewise linear minimization problem and the related optimization techniques are applicable . Moreover , current AR approaches perform well only when the length of the time-series is much larger than the number of brain sites studied , which is exactly the reverse of the situation in neuroimaging whereby relatively short time-series are measured over thousands of voxels thus the need for penalized methods to obtain sparse solutions . In this paper , we introduce a novel ADMM-based AR estimator termed LAPPS ( Least Absolute LP ( 0 < p < 1 ) Penalized Solution ) which employs the L1-loss function for the residual error to alleviate the influence of outliers and another Lp-penalty term ( p = 0.5 ) to obtain the sparse AR parameters while suppressing any spurious noise that may be present . Our obtained simulations result quantitatively show that LAPPS-AR performs better than the commonly used AR estimation methods . Due to the non-smoothness of the L1-loss function in the two models , most of the traditional approaches focus on solving the dual problem . In this paper , we propose an augmented Lagrangian method for the L1-loss model , which is designed to solve the primal problem . By tackling the non-smooth term in the model with Moreau – Yosida regularization and the proximal operator , the subproblem in augmented Lagrangian method reduces to a non-smooth linear system , which can be solved via the quadratically convergent semismooth Newton 's method . Under an annotation generation model that takes this into account , there is a hazard that some of the training instances are actually hard cases with unreliable annotations . We show that these are relatively unproblematic for an algorithm operating under the 0 - -1 loss model , whereas for the commonly used voted perceptron algorithm , hard training cases could result in incorrect prediction on the uncontroversial cases at test time . In the preprocessing stage , we first design a fully convolutional neural network for preprocessing ( pFCN ) . To fit the preprocessing task , the pFCN is trained by an L1 based total loss function , which is simple yet could enhance the facial features . Then the full-size photo is fed to the well-trained pFCN to generate the feature map , which we call a semi-sketch since it bridges the discrepancy between photo and sketch . The L1/2 regularization method isa reweighed iterative algorithm with the adaptively weighted L1 penalty on regression coefficients . The algorithm of theL1/2 regularization method can be easily obtained by a series of L1 penalties . Simulation results based on standard artificial data show that theL1/2 regularization method can be more accurate for variable selection than Lasso and adaptive Lasso methods . Implicit PRF-based approaches , such as graph-based and Bayesian visual reranking , deal with such unreliability by making use of the initial ranking in a soft manner , but have limited capability of promoting relevant images and lowering down irrelevant images . In this paper , we propose l1 square loss optimization based on sparsity and ranking constraints to detect confident samples which are most likely to be relevant to a query . Based on the discovered confident samples , we present an adaptive kernel-based scheme to rerank the images . The paper describes a heuristic algorithm for the asymmetric travelling salesman problem . The procedure is a generalization of Webb 's Simple Loss 1 Method and is of the sequential type , i.e. in each step one link of the tour is fixed . The method proved to produce " high quality " near optimal solutions in a computing time , which only grows proportional to n1.82 . The PixelGAN has no effect on spatial sharpness but does increase the colorfulness of the results ( quantified in Figure [ reference ] ) . For example , the bus in Figure [ reference ] is painted gray when the net is trained with an L1 loss , but becomes red with the PixelGAN loss . Color histogram matching is a common problem in image processing , and PixelGANs may be a promising lightweight solution . The cost-function of this transportation problem depends on which distance-function one uses to measure distances between pixels . In this paper we present an algorithm , with a computational complexity of roughly order $ $ \mathcal{O}$$ ( N2 ) , where N is equal to the number of pixels in the two images , in case the underlying distance-function is the L1-metric , an approximation of the L2-metric or the square of the L2-metric ; a standard algorithm would have a computational complexity of order $ $ \mathcal{O}$$ ( N3 ) . The algorithm is based on the classical primal-dual algorithm . For unimodal regression on n weighted points our algorithm for the L"2 metric requires only @Q(n ) time , while for the L"1 metric it requires @Q(nlogn ) time . For unweighted points our algorithm for the L"~ metric requires only @Q(n ) time . All of these times are optimal . Abstract This paper presents a bi-objective MILP model for the vehicle routing and scheduling problem with cross-docking for perishable products . It is demonstrated that considering merely one objective sacrifices the other one and that the L1 metric method makes a suitable trade-off . Two robust models are developed when the travel time of the outbound vehicles and the freshness-life of the products are uncertain . Oleh karena itu , diperlukan sebuah sistem yang dapat membaca aksara Jepang . Penelitian ini akan difokuskan pada perancangan aplikasi pengenalan karakter optik aksara Jepang dengan menggunakan fitur matriks populasi piksel dan l1-metric distance dalam melakukan pengenalan pada citra aksara Jepang dimana fitur matriks populasi piksel digunakan untuk mendapatkan fitur dari karakter dan l1-metric distance untuk pengukuran jarak . Serta dalam membangun aplikasi digunakan bahasa pemograman java . ? We study the distance measures between two probability distributions via two different distance metrics , a new metric induced from Jensen-Shannon divergence , and the well known L/sub 1/ metric . We show that several important results and constructions in computational complexity under the L/sub 1/ metric carry over to the new metric , such as Yao 's next-bit predictor , the existence of extractors , the leftover hash lemma , and the construction of expander graph based extractor . If a set of sparse features could be identified for these techniques , the resulting data may improve SVDD performance while enjoying the benefits of decreased processing overhead . Although band selection has been investigated in previous efforts , this work builds on recent research that has resulted in the development of a theoretical framework for signal classification with sparse representation using L1 measures . In this model , the contour is implicitly represented by the discontinuity of a binary level set function instead of the zero-level set . The energy functional consists of three terms , i.e. , the data term that is defined by L1-norm metric , the regularization term that is defined by Dirichlet energy of level set function , and the penalty term that punishes the deviation of level set function from binary function . We design a three-step time-splitting scheme to solve the gradient descent flow of the proposed model efficiently , in which the flow equation is divided into three differential equations that are solved sequentially and alternatively until convergence . Our method uses a hybrid approach to sampling that combines traditional graph discretization with random sampling . We present techniques that optimize the graph for the L1-norm metric function commonly used in joint-based robotic planning , purposefully choosing a $ t$-stretch factor based on the geometry of the space , and removing redundant edges that do not contribute to the graph quality . A high-quality pre-processed sparse roadmap is then available for re-use across many different planning scenarios using standard repair and re-plan methods . While the performance of the log likelihood baseline suffered as the amount of contamination was increased , many of the distributional approaches were not affected . We found that the L1 distance , χ2 statistic , and information theory divergences consistently outperformed the other methods for a variety of contamination levels and test segment lengths . In this paper , we propose a novel approximation method for fast Gaussian convolution of two-dimensional uniform point sets , such as 2D images . Our method employs L1 distance metric for Gaussian function and domain splitting approach to achieve fast computation ( linear computational complexity ) while preserving high accuracy . Our numerical experiments show the advantages over conventional methods in terms of speed and precision . There are mainly two classes of discrepancy measures : distance measures based on the density difference , such as the Lp-distances , and divergence measures based on the density ratio , such as the Φ-divergences . The intersection of these two classes is the L1-distance measure , and thus , it can be estimated either based on the density difference or the density ratio . In this paper , we first show that the Bregman scores , which are widely employed for the estimation of probability densities in statistical data analysis , allows us to estimate the density difference and the density ratio directly without separately estimating each probability distribution . The proposed systems are evaluated with the ImageCLEF-2011 benchmarking dataset . Our experimental results demonstrate the advantage of the proposed metric learning approach and L1 distance-based measures . In this paper , we consider the rectilinear distance location problem with box constraints ( RDLPBC ) and we show that RDLPBC can be reduced to the rectilinear distance location problem ( RDLP ) . A necessary and sufficient condition of optimality is provided for RDLP . This research refers to two types of calculation methods . First , manual calculation , which is rectilinear distance measure . Second , computerized calculation , which is Quantitative Systems ( QS ) software . In this paper we introduce a new approach to these problems , based on zero-skew “ stretching ” of spanning trees , and obtain algorithms with improved approximation factors of 4 and 14 . For the case when tree nodes are points in the plane and edge weights are given by the rectilinear metric our algorithms find zero- and bounded-skew trees of length at most 3 and 9 times the optimum . This case is of special interest in VLSI clock routing . The minimax hub location problem sites a facility to minimize the maximum weighted interaction cost between pairs of fixed nodes . In this paper , distances are represented by a rectilinear norm and may be suited to factory layout or street network problems . The problem is already well known ( in 2-D ) as the round trip location problem and is extended to 3-D in this paper . Furthermore , the quadcopter is equipped with a logger that records all the flight information , which is used to geocode the video data . In a first step , the video data are corrected for the distortion of the lens and are then geocoded to a rectilinear coordinate system at water level . The resulting video data allow for measuring surface wave properties such as wave direction and length as well as phase velocity . In this research use POM QM Software for validating total distances of material handling . Analysis result and designing facilities layout is a new layout with Rectilinear Distance method , The initial movement distances is 1.922,9 m become 1.832,35 m or have a reduction distances 4,94 % from initial layout . While for the initial total cost of material handling is Rp 200.135,6 become Rp 199.745,9 or have a reduction cost 0,19 % from initial layout . Abstract The unit costs of up- and down-adjustments are not equal in some real-life minimum cost consensus ( MCC ) problems , such as when each individual has two different cost coefficients that depend on the adjustment direction of his/her opinion . To solve these problems , the MCC model with directional constraints ( MCCM-DC ) is constructed on the basis of goal programming theory and the rectilinear distance function . To analyse the impact of individuals ’ limited compromises and tolerance behaviors on the consensus modeling , we further develop the e-MCCM-DC and the threshold-based (TB)-MCCM-DC . The case study is the University of Ilorin academic environment where transmission environmental monitoring system equipment was used to monitor the signals on the existing BTSs , to ascertain the positions of the existing BTSs , the total number of the existing BTSs , and areas of poor signal strengths for all the existing operators . The electric field intensity of each of the BTS sites was determined and was used as the weight in the rectilinear distance location model . The operator with the poorest signal strength was selected to occupy the new BTS so as to improve its handover and network connectivity . Given a set of origin-destination points in the plane and a set of polygonal barriers to travel , this paper develops an efficient algorithm for finding minimal distance feasible paths between the points , assuming that all travel occurs according to the rectilinear distance metric . By geometrical arguments the problem is reduced to a finite network problem .
In cases where the number of features is large , feature selection is desirable to enhance model interpretability and to avoid overfitting . In this paper , we propose a robust feature selection method via l2,1-norm penalized maximum likelihood estimation ( MLE ) in FMR , with extension to sparse l2,1 penalty by combining l1-norm with l2,1-norm for increasing flexibility . To solve the non-convex and non-smooth problem of ( sparse ) penalized MLE in FMR , we develop an new EM-based algorithm for numerical optimization , with combination of block coordinate descent and majorizing-minimization scheme in M-step .
Numerical simulation results indicate that the proposed approach lowers the mean square error of retrieved excitations compared to the non-convex approach using perturbation technique , with the advantage of a significant reduction of running time . In addition , it also improves the probability of success rate of diagnosis effectively compared to the l 1 norm and reweighted l 1 norm regularised methods .
For M/EEG-based distributed source imaging , it has been established that the L2-norm-based methods are effective in imaging spatially extended sources , whereas the L1-norm-based methods are more suited for estimating focal and sparse sources . However , when the spatial extents of the sources are unknown a priori , the rationale for using either type of methods is not adequately supported .
The median filter term makes our method eliminate additional noise from the reconstruction process and obtain much clearer reconstruction results . One key point of the proposed method lies in the fact that both the total variation term and the median filter term are presented in the L1 norm formulation . We then apply the split Bregman technique for fast minimization and give an efficient algorithm .
A new nonlinear constrained L1 norm problem is constructed by taking the increment of component values and the voltage increment at inaccessible nodes in the faulty circuit for variables to be optimized . This L1 norm problem is solved based on the principle of Hopfield neural network . Using this technique , only a single process of optimization is needed to isolate the most likely faulty elements with minimal computation .
It is wellknown that the l1-norm is robust to outliers ( Brooks , Dul , and Boone 2013 ; Meng , Zhao , and Xu 2012 ) . In order to alleviate this effect , Ke and Kanade ( Ke and Kanade 2005 ) proposed the robust PCA called PCA-L1 , which uses l1norm to minimize the reconstruction error . PCA-L1 achieves robust results , however , it usually gets the local optimal solution due to the discontinuity of the objective function .
In this paper , we propose a robust kernel NMF approach using L2 , 1 norm loss function . Compared with the standard NMF algorithm , the new robust kernel NMF updating algorithm is as elegant and as simple , but with the newly added robustness to handle significantly corrupted datasets because of using L2 , 1 norm . Experiments on normal and occluded datasets indicate that robust kernel NMF always perform better than k-means and standard NMF .
However , traditional PCA minimizes the sum of squared errors and suffers from both outliers and large feature noises . The L1-norm based PCA ( more precisely L1,1 norm ) is more robust . Yet , the optimization on L1-PCA is much harder than standard PCA .
We discuss a general strategy to construct coherence measures . One can build an important class of coherence measures which cover the relative entropy measure for pure states , the l1-norm measure for pure states and the α-entropy measure . The optimal conversion of coherent states under incoherent operations is presented which sheds some light on the coherence of a single copy of a pure state .
Commonly used constraints include the L1 / L2 norm and cosine similarity . We adopt the following loss function based on the L2 norm , which was originally proposed by Hadsell _ meaning : NTF .
K-means algorithm in clustering is used to find the distance between the nodes or objects and the cluster centroids . The distance is calculated by either Euclidean distance , Manhattan distance or Minkowski distance . The grouping of objects is done on the basis of distance between the object and the cluster centroids(1 ) .
Indian premier league dataset has been considered for the detection of clusters . Several clustering parameters like centroid , radius and Manhattan distance measure have been applied . The definition of clusters as well as the membership function has been implemented using PL/SQL .
One of the most important information that an intelligent agent can have , is a representation of the environment , not necessarily in the form of a map , but of a topological graph of the plane , which can be used to perform efficient planning . This work proposes a method to produce a topological graph of an Occupancy Grid Map ( OGM ) by using a Manhattan distance function to create the Approximate Generalized Voronoi Diagram ( AGVD ) . Several improvements in the AGVD are made , in order to produce a crisp representation of the spaces skeleton , but in the same time to avoid the complex results of other methods .
Four weight values are calculated for each learning object , describing how related the learner and the learning object in question is at each dimension of Felder and Silverman learning styles model . Then , weight values for each dimension is combined by using Manhattan distance metric to provide a single weight value as a fitness function representing the general relatedness of the learner and the learning object . Results of the personalization approach can be used to recommend learning objects ordered according to their weight values to the learners .
A neuron MOS association processor searches for the event in the past memory having the maximum similarity to the current event presented to the system . This is based on Manhattan-distance calculation and the minimum distance search by a winner-take-all ( WTA ) circuitry . The computation is carried out directly on the hardware in a fully parallel architecture .
By using the Manhattan distance concept , this study shows that the formulation of the algorithm is correct and in line with results in the literature . Hence , the Manhattan distance concept may be useful in other scheduling and optimization problems . Furthermore , this unique concept makes it possible to develop a more sustainable telecommunication system with ease of connectivity among users , even when several subscribers are on a common frequency .
The color-based road detector works in the HSV color space and uses RGB normalization and white balance algorithms to reduce illuminations effects . Also , it uses a Manhattan distance measurement to segment the image into road pixels and a mean-shift algorithm to keep track of the road color characteristics . The texture line detector applies an inverse perspective mapping ( IPM ) to get rid of the perspective effects and detect parallel road borders based on a canny edge detector combined with a Hough transform .
Reducing computational complexity and communication overhead is essential in order to reduce power consumption , extend the life time of the battery operated nodes , and improve the performance of the limited computational resources of these sensor nodes . To that end , we propose a novel modified Manhattan distance norm and employ it in a previous localization system so-called TALS ( Trigonometric Ad-hoc Localization System ) , such that we optimize TALS . Furthermore , an analysis and an extensive simulation for the optimized TALS ( OTALS ) is presented showing its cost , accuracy , and efficiency , thus deducing the impact of its parameters on performance .
And how to maintain the image quality becomes the important issue . In this work , we propose to use a well-known video quality metric called VQM ( Video Quality Metric ) to measure the video quality , and proposed a VQM curve to be predicted by the MSE ( Mean Square Error ) . This proposed curve can later be used to efficiently maintain the video quality while performing the backlight power saving .
Further , it is compared with single exponential smoothing ; double exponential smoothing with trend ; and Holt-Winter multiplicative and additive seasonal and multiple linear regression models . The performance of the models was measured by using various error functions such as root mean square error , mean absolute error , and mean absolute percentage error . The validation tests of the models were also carried out using different goodness of fit measures such as R-square , bias and tracking signal .
metode Economic Order Quantity ( EOQ ) dengan dukungan perangkat PC untuk menghasilkan kinerja yang lebih optimal maka rancangan ini dibuat dengan menggunakan Software dengan bahasa pemrograman Matlab R2010a dan sistem operasi Windows 7 . K ata kunci : LPG , Mean Absolute Percentage Error , Economic Order Quantity ( EOQ ) , Matlab
In embedded system , the speed of sweep fingerprint sequences stitching is very important . This paper proposes a fingerprint sequences algorithm , based on motion estimation and minimum mean absolute error . The algorithm ensures effective fingerprint stitching , reduces complexity , and improves its efficiency .
The proposed FDB-HRT algorithm was 99.16 % and 99.2 % accurate with regard to heart rate measurement and fall detection , respectively . In addition , the geolocation error of patient fall incidents based on a GPS module was evaluated by mean absolute error analysis for 17 different locations in three cities in Iraq . Mean absolute error was 1.08 × 10−5 ° and 2.01 × 10−5 ° for latitude and longitude data relative to data from the GPS Benchmark system .
Three measures are analyzed on a 20.2-mile long corridor in San Antonio , Texas , including corridor travel time , corridor travel-time reliability , and day-to-day variation in travel time . The primary contributions of this article are the analysis of the impact of using two different approaches for travel-time aggregation across segments — instantaneous and time-dependent approaches — and defining a mean absolute error-based method to identify days when travel times significantly deviate from typical traffic conditions . The findings suggest that the temporal patterns of corridor travel times obtained using instantaneous and time-dependent aggregation approaches are similar ; however , instantaneous travel-time estimates lead to inaccuracies that become more apparent during peak hours and for longer segments .
In an effort to avoid complexities introduced by more advanced learning algorithms , the simple backpropagation with momentum algorithm has been employed . A member of classification problems were solved with neural networks that have been trained with the usual mean square error function , the mean absolute error function , the cross-entropy or maximum likelihood function , the Kalman-Kwasny error function ( 1991 ) , as well as a novel error function designed by the authors . The results indicate that , in most problems examined , an error function other than the usual mean square gives a better performance , both in terms of the number of epochs needed for training , as well as the obtained generalization ability of the trained network .
The Belief Distribution Algorithm retains the qualities of nearest-neighbor algorithms which have performed well in the past , yet produces predictions of belief distributions across rating values rather than a point rating value . In addition , we illustrate how the exclusive use of the mean absolute error metric has concealed these flaws for so long , and we propose the use of a modified Precision metric for more accurately evaluating the user experience .
However , N-PLS by itself does not perform variable selection , which indeed can facilitate interpretation in different situations ( e.g. the so-called “ –omics ” data ) . In this work , we propose a method for variable selection within N-PLS by introducing sparsity in the weights matrices WJ and WK by means of L1-penalization . The sparse version of N-PLS is able to provide lower prediction errors by filtering all the noise variables and to further improve interpretability and usability of the N-PLS results .
The effectiveness of local search comes from the piecewise linearity of the ramp loss . Motivated by the fact that the l1-penalty is piecewise linear as well , the l1-penalty is applied for the ramp loss , resulting in a ramp loss linear programming support vector machine ( ramp-LPSVM ) . The proposed ramp-LPSVM is a piecewise linear minimization problem and the related optimization techniques are applicable .
Moreover , current AR approaches perform well only when the length of the time-series is much larger than the number of brain sites studied , which is exactly the reverse of the situation in neuroimaging whereby relatively short time-series are measured over thousands of voxels thus the need for penalized methods to obtain sparse solutions . In this paper , we introduce a novel ADMM-based AR estimator termed LAPPS ( Least Absolute LP ( 0 < p < 1 ) Penalized Solution ) which employs the L1-loss function for the residual error to alleviate the influence of outliers and another Lp-penalty term ( p = 0.5 ) to obtain the sparse AR parameters while suppressing any spurious noise that may be present . Our obtained simulations result quantitatively show that LAPPS-AR performs better than the commonly used AR estimation methods .
Due to the non-smoothness of the L1-loss function in the two models , most of the traditional approaches focus on solving the dual problem . In this paper , we propose an augmented Lagrangian method for the L1-loss model , which is designed to solve the primal problem . By tackling the non-smooth term in the model with Moreau – Yosida regularization and the proximal operator , the subproblem in augmented Lagrangian method reduces to a non-smooth linear system , which can be solved via the quadratically convergent semismooth Newton 's method .
Under an annotation generation model that takes this into account , there is a hazard that some of the training instances are actually hard cases with unreliable annotations . We show that these are relatively unproblematic for an algorithm operating under the 0 - -1 loss model , whereas for the commonly used voted perceptron algorithm , hard training cases could result in incorrect prediction on the uncontroversial cases at test time .
In the preprocessing stage , we first design a fully convolutional neural network for preprocessing ( pFCN ) . To fit the preprocessing task , the pFCN is trained by an L1 based total loss function , which is simple yet could enhance the facial features . Then the full-size photo is fed to the well-trained pFCN to generate the feature map , which we call a semi-sketch since it bridges the discrepancy between photo and sketch .
The L1/2 regularization method isa reweighed iterative algorithm with the adaptively weighted L1 penalty on regression coefficients . The algorithm of theL1/2 regularization method can be easily obtained by a series of L1 penalties . Simulation results based on standard artificial data show that theL1/2 regularization method can be more accurate for variable selection than Lasso and adaptive Lasso methods .
Implicit PRF-based approaches , such as graph-based and Bayesian visual reranking , deal with such unreliability by making use of the initial ranking in a soft manner , but have limited capability of promoting relevant images and lowering down irrelevant images . In this paper , we propose l1 square loss optimization based on sparsity and ranking constraints to detect confident samples which are most likely to be relevant to a query . Based on the discovered confident samples , we present an adaptive kernel-based scheme to rerank the images .
The paper describes a heuristic algorithm for the asymmetric travelling salesman problem . The procedure is a generalization of Webb 's Simple Loss 1 Method and is of the sequential type , i.e. in each step one link of the tour is fixed . The method proved to produce " high quality " near optimal solutions in a computing time , which only grows proportional to n1.82 .
The PixelGAN has no effect on spatial sharpness but does increase the colorfulness of the results ( quantified in Figure [ reference ] ) . For example , the bus in Figure [ reference ] is painted gray when the net is trained with an L1 loss , but becomes red with the PixelGAN loss . Color histogram matching is a common problem in image processing , and PixelGANs may be a promising lightweight solution .
The cost-function of this transportation problem depends on which distance-function one uses to measure distances between pixels . In this paper we present an algorithm , with a computational complexity of roughly order $ $ \mathcal{O}$$ ( N2 ) , where N is equal to the number of pixels in the two images , in case the underlying distance-function is the L1-metric , an approximation of the L2-metric or the square of the L2-metric ; a standard algorithm would have a computational complexity of order $ $ \mathcal{O}$$ ( N3 ) . The algorithm is based on the classical primal-dual algorithm .
For unimodal regression on n weighted points our algorithm for the L"2 metric requires only @Q(n ) time , while for the L"1 metric it requires @Q(nlogn ) time . For unweighted points our algorithm for the L"~ metric requires only @Q(n ) time . All of these times are optimal .
Abstract This paper presents a bi-objective MILP model for the vehicle routing and scheduling problem with cross-docking for perishable products . It is demonstrated that considering merely one objective sacrifices the other one and that the L1 metric method makes a suitable trade-off . Two robust models are developed when the travel time of the outbound vehicles and the freshness-life of the products are uncertain .
Oleh karena itu , diperlukan sebuah sistem yang dapat membaca aksara Jepang . Penelitian ini akan difokuskan pada perancangan aplikasi pengenalan karakter optik aksara Jepang dengan menggunakan fitur matriks populasi piksel dan l1-metric distance dalam melakukan pengenalan pada citra aksara Jepang dimana fitur matriks populasi piksel digunakan untuk mendapatkan fitur dari karakter dan l1-metric distance untuk pengukuran jarak . Serta dalam membangun aplikasi digunakan bahasa pemograman java . ?
We study the distance measures between two probability distributions via two different distance metrics , a new metric induced from Jensen-Shannon divergence , and the well known L/sub 1/ metric . We show that several important results and constructions in computational complexity under the L/sub 1/ metric carry over to the new metric , such as Yao 's next-bit predictor , the existence of extractors , the leftover hash lemma , and the construction of expander graph based extractor .
If a set of sparse features could be identified for these techniques , the resulting data may improve SVDD performance while enjoying the benefits of decreased processing overhead . Although band selection has been investigated in previous efforts , this work builds on recent research that has resulted in the development of a theoretical framework for signal classification with sparse representation using L1 measures .
In this model , the contour is implicitly represented by the discontinuity of a binary level set function instead of the zero-level set . The energy functional consists of three terms , i.e. , the data term that is defined by L1-norm metric , the regularization term that is defined by Dirichlet energy of level set function , and the penalty term that punishes the deviation of level set function from binary function . We design a three-step time-splitting scheme to solve the gradient descent flow of the proposed model efficiently , in which the flow equation is divided into three differential equations that are solved sequentially and alternatively until convergence .
Our method uses a hybrid approach to sampling that combines traditional graph discretization with random sampling . We present techniques that optimize the graph for the L1-norm metric function commonly used in joint-based robotic planning , purposefully choosing a $ t$-stretch factor based on the geometry of the space , and removing redundant edges that do not contribute to the graph quality . A high-quality pre-processed sparse roadmap is then available for re-use across many different planning scenarios using standard repair and re-plan methods .
While the performance of the log likelihood baseline suffered as the amount of contamination was increased , many of the distributional approaches were not affected . We found that the L1 distance , χ2 statistic , and information theory divergences consistently outperformed the other methods for a variety of contamination levels and test segment lengths .
In this paper , we propose a novel approximation method for fast Gaussian convolution of two-dimensional uniform point sets , such as 2D images . Our method employs L1 distance metric for Gaussian function and domain splitting approach to achieve fast computation ( linear computational complexity ) while preserving high accuracy . Our numerical experiments show the advantages over conventional methods in terms of speed and precision .
There are mainly two classes of discrepancy measures : distance measures based on the density difference , such as the Lp-distances , and divergence measures based on the density ratio , such as the Φ-divergences . The intersection of these two classes is the L1-distance measure , and thus , it can be estimated either based on the density difference or the density ratio . In this paper , we first show that the Bregman scores , which are widely employed for the estimation of probability densities in statistical data analysis , allows us to estimate the density difference and the density ratio directly without separately estimating each probability distribution .
The proposed systems are evaluated with the ImageCLEF-2011 benchmarking dataset . Our experimental results demonstrate the advantage of the proposed metric learning approach and L1 distance-based measures .
In this paper , we consider the rectilinear distance location problem with box constraints ( RDLPBC ) and we show that RDLPBC can be reduced to the rectilinear distance location problem ( RDLP ) . A necessary and sufficient condition of optimality is provided for RDLP .
This research refers to two types of calculation methods . First , manual calculation , which is rectilinear distance measure . Second , computerized calculation , which is Quantitative Systems ( QS ) software .
In this paper we introduce a new approach to these problems , based on zero-skew “ stretching ” of spanning trees , and obtain algorithms with improved approximation factors of 4 and 14 . For the case when tree nodes are points in the plane and edge weights are given by the rectilinear metric our algorithms find zero- and bounded-skew trees of length at most 3 and 9 times the optimum . This case is of special interest in VLSI clock routing .
The minimax hub location problem sites a facility to minimize the maximum weighted interaction cost between pairs of fixed nodes . In this paper , distances are represented by a rectilinear norm and may be suited to factory layout or street network problems . The problem is already well known ( in 2-D ) as the round trip location problem and is extended to 3-D in this paper .
Furthermore , the quadcopter is equipped with a logger that records all the flight information , which is used to geocode the video data . In a first step , the video data are corrected for the distortion of the lens and are then geocoded to a rectilinear coordinate system at water level . The resulting video data allow for measuring surface wave properties such as wave direction and length as well as phase velocity .
In this research use POM QM Software for validating total distances of material handling . Analysis result and designing facilities layout is a new layout with Rectilinear Distance method , The initial movement distances is 1.922,9 m become 1.832,35 m or have a reduction distances 4,94 % from initial layout . While for the initial total cost of material handling is Rp 200.135,6 become Rp 199.745,9 or have a reduction cost 0,19 % from initial layout .
Abstract The unit costs of up- and down-adjustments are not equal in some real-life minimum cost consensus ( MCC ) problems , such as when each individual has two different cost coefficients that depend on the adjustment direction of his/her opinion . To solve these problems , the MCC model with directional constraints ( MCCM-DC ) is constructed on the basis of goal programming theory and the rectilinear distance function . To analyse the impact of individuals ’ limited compromises and tolerance behaviors on the consensus modeling , we further develop the e-MCCM-DC and the threshold-based (TB)-MCCM-DC .
The case study is the University of Ilorin academic environment where transmission environmental monitoring system equipment was used to monitor the signals on the existing BTSs , to ascertain the positions of the existing BTSs , the total number of the existing BTSs , and areas of poor signal strengths for all the existing operators . The electric field intensity of each of the BTS sites was determined and was used as the weight in the rectilinear distance location model . The operator with the poorest signal strength was selected to occupy the new BTS so as to improve its handover and network connectivity .
Given a set of origin-destination points in the plane and a set of polygonal barriers to travel , this paper develops an efficient algorithm for finding minimal distance feasible paths between the points , assuming that all travel occurs according to the rectilinear distance metric . By geometrical arguments the problem is reduced to a finite network problem .
Robust feature selection via l2, 1-norm in finite mixture of regression Fast non-convex compressed sensing approach for diagnosis of defective array elements using planar near-field measurements STRAPS: A Fully Data-Driven Spatio-Temporally Regularized Algorithm for M/EEG Patch Source Imaging Median Filter Based Compressed Sensing Model with Application to MR Image Reconstruction A New Technique for Fault Isolation in Linear Analog Circuits Locality Preserving Projection Based on F-norm Robust Kernel Nonnegative Matrix Factorization An efficient algorithm for L1-norm principal component analysis Coherence measures and optimal conversion for coherent states Deep Learning Face Representation by Joint Identification-Verification Sharing of Clusters for different Nodes in Graph Computing Cluster Detection Analysis Using Fuzzy Relational Database Construction of Minimized Topological Graphs on Occupancy Grid Maps Based on GVD and Sensor Coverage Information A Felder and Silverman Learning Styles Model Based Personalization Approach to Recommend Learning Objects Event recognition hardware based on neuron MOS soft-computing circuits Sustainable Communication Systems: A Graph-Labeling Approach for Cellular Frequency Allocation in Densely-Populated Areas Vision-based road boundary tracking system for unstructured roads A modified Manhattan distance with application for localization algorithms in ad-hoc WSNs Video quality metric model of MSE for backlight power saving Forecasting exchange rate of Solomon Islands dollar against Euro using artificial neural network Metode Economic Order Quantity Untuk Memprediksi Penerimaan Dan Penyaluran LPG Sweep Fingerprint Sequences Stitching Based on Motion Estimation An Advanced First Aid System Based on an Unmanned Aerial Vehicles and a Wireless Body Area Sensor Network for Elderly Persons in Outdoor Environments Using National Performance Management Research Data Set for Corridor Performance Measures: A US-281N Corridor Case Study The impact of the error function selection in neural network-based classifiers A collaborative filtering algorithm and evaluation metric that accurately model the user experience Sparse N-way Partial Least Squares by L1-penalization Ramp loss linear programming support vector machine Sparse Autoregressive Modeling via the Least Absolute LP-Norm Penalized Solution An efficient augmented Lagrangian method for support vector machine Learning with Annotation Noise FCN based preprocessing for exemplar-based face sketch synthesis Iterative L1/2 Regularization Algorithm for Variable Selection in the Cox Proportional Hazards Model Robust visual reranking via sparsity and ranking constraints Heuristic for the Asymmetric Travelling Salesman Problem Image-to-Image Translation with Conditional Adversarial Networks Computing the Kantorovich Distance for Images Unimodal regression via prefix isotonic regression The vehicle routing and scheduling problem with cross-docking for perishable products under uncertainty: Two robust bi-objective models OPTICAL CHARACTER RECOGNITION JEPANG MENGGUNAKAN MATRIKS POPULASI PIKSEL DAN L1-METRIC On the Jensen-Shannon Divergence and Variational Distance Sparse feature extraction for Support Vector Data Description applications A binary level set variational model with L1 data term for image segmentation Sparser Sparse Roadmaps Coping with training contamination in unsupervised distributional anomaly detection Быстрое L1-преобразование Гаусса для сглаживания изображений с сохранением границ Statistical Analysis of Distance Estimators with Density Differences and Density Ratios Learning metrics for content-based medical image retrieval A fast algorithm for the rectilinear distance location problem USULAN TATA LETAK ULANG MENGGUNAKAN SOFTWARE QUANTITATIVE SYSTEMS UNTUK MEMINIMALKAN JARAK PERPINDAHAN BAHAN DI LANTAI PRODUKSI DEPARTEMEN MECHANIC PT JEFTA PRAKARSA PRATAMA Practical approximation algorithms for zero- and bounded-skew trees Rectilinear minimax hub location problems Surface currents retrieved from airborne video ANALISIS TATA LETAK FASILITAS DENGAN MENGGUNAKAN METODE ACTIVITY RELATIONSHIP CHART PADA INDUSTRI MEBEL BAMBU KARYA MANUNGGAL YOGYAKARTA Modeling the minimum cost consensus problem in an asymmetric costs context Base transceiver station deployment using rectilinear distance location of a single facility Finding minimum rectilinear distance paths in the presence of barriers
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AAAI ICDM Workshops ICASSP NIPS ICCSA (5) IJCNN SIGIR ACL/IJCNLP ICSI (2) ACM Multimedia 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) IGARSS CISS EMBC SODA IGARSS
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