The use of the alternating direction method of multipliers (ADMM) algorithm to train a classifier may reduce the amount of classifier training time with little degradation in classifier accuracy. The training involves partitioning the training data for training the classifier into multiple data blocks. The partitions may preserve the joint distribution of input features and an output class of the training data. The training may further include performing an ADMM iteration on the multiple data blocks in an initial order using multiple worker nodes. Subsequently, the training of the classifier is determined to be completed if a stop criterion is satisfied following the ADMM iteration. Otherwise, if the stop criterion is determined to be unsatisfied following the ADMM iteration, one or more additional ADMM iterations may be performed on different orders of the multiple data blocks until the stop criterion is satisfied.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. One or more computer-readable media storing computer-executable instructions that are executable to cause one or more processors to perform acts comprising: partitioning training data for training a classifier into multiple data blocks that preserve joint distribution of input features and an output class of the training data; distributing the multiple data blocks to a plurality of worker nodes such that the plurality of worker nodes perform an alternating direction method of multipliers (ADMM) iteration on the multiple data blocks in an initial order to train the classifier; providing the multiple data blocks to the plurality of worker nodes such that the plurality of worker nodes perform an additional ADMM iteration on the multiple data blocks in a different order to train the classifier in response to a stop criterion being unsatisfied following the ADMM iteration; and determining that the classifier is trained into a trained classifier in response to the stop criterion being satisfied following the ADMM iteration. 2. The one or more computer-readable media of claim 1, further comprising classifying input data via the trained classifier to generate output data. 3. The one or more computer-readable media of claim 2, wherein the classifying the input data includes performing one of speech recognition, image recognition, handwriting analysis, or object classification. 4. The one or more computer-readable media of claim 1, wherein the classifier includes deep neural networks. 5. The one or more computer-readable media of claim 1, wherein the plurality of worker nodes includes a plurality of central processing units or a plurality of graphics processing units. 6. The one or more computer-readable media of claim 1, wherein the multiple data blocks are non-overlapping data blocks. 7. The one or more computer-readable media of claim 1, wherein a size of each data block of the multiple data blocks is based on at least one of a volume of the training data processed, a number of worker nodes processing the training data, data processing capabilities of the multiple worker nodes, and a desired number of the multiple data blocks. 8. The one or more computer-readable media of claim 1, wherein the determining includes ascertaining that the stop criterion is satisfied in response to a relative change in an objective function associated with the ADMM iteration being less than a predetermined change threshold, a classification accuracy of the trained classifier with respect to a validation data set being within a predetermined percentage of a baseline classification accuracy generated by a baseline classifier for the validation data set, or a model parameter of a global model associated with the classifier reaching a predetermined value. 9. The one or more computer-readable media of claim 1, wherein the partitioning the training data includes: partitioning the training data into data subsets based on class labels of feature vectors in the training data, each data subset having data with corresponding feature vectors that share a common class label; performing a k-means clustering of the feature vectors in the data subsets to form a plurality of data clusters; and partitioning the feature vectors of the data clusters randomly into the multiple data blocks. 10. The computer-readable medium of claim 1, wherein a performance of the ADMM iteration or a performance of the additional ADMM iteration includes: partitioning a data block of the multiple data blocks into multiple data subsets; distributing the multiple data subsets and a global model to the multiple worker nodes, each of the worker nodes having a local model; receiving updated local models corresponding to the multiple data subset from the multiple worker nodes, each updated local model being updated independently at a corresponding worker node using a single-sweep mini-batch based stochastic gradient descent processing of a corresponding data subset; and aggregating the updated local models to update the global model. 11. The computer-readable medium of claim 10, wherein a quantity of the multiple data subsets and a quantity of the worker nodes are scalable based on a size of the training data. 12. A computer-implemented method, comprising: partitioning training data for training deep neural networks (DNNs) into multiple non-overlapping data blocks that preserve joint distribution of input features and an output class of the training data; performing an alternating direction method of multipliers (ADMM) iteration on the multiple non-overlapping data blocks in an initial order using multiple worker nodes to train the DNNs; determining that the DNNs are trained into trained DNNs in response to a stop criterion being satisfied following the ADMM iteration; and classifying input data via the trained DNNs to generate output data. 13. The computer-implemented method of claim 12, further comprising performing an additional ADMM iteration on the multiple non-overlapping data blocks in a different order in response to the stop criterion being unsatisfied following the ADMM iteration. 14. The computer-implemented method of claim 12, wherein the classifying the input data includes performing one of speech recognition, image recognition, handwriting analysis, or object classification. 15. The computer-implemented method of claim 12, wherein the determining includes ascertaining that the stop criterion is satisfied in response to a relative change in an objective function associated with the ADMM iteration being less than a predetermined change threshold, a classification accuracy of the trained DNNs with respect to a validation data set being within a predetermined percentage of a baseline classification accuracy generated by a baseline classifier for the validation data set, or a model parameter of a global model associated with the classifier reaching a predetermined value. 16. The computer-implemented method of claim 12, wherein the performing the ADMM iteration includes: partitioning a data block of the multiple non-overlapping data blocks into multiple data subsets; distributing the multiple data subsets and a global model to the multiple worker nodes; updating a local model of a corresponding data subset at each of the multiple worker nodes independently using a single-sweep mini-batch based stochastic gradient descent processing of the corresponding data subset; and aggregating multiple local models to update the global model. 17. A system, comprising: a plurality of processors; a memory that includes a plurality of computer-executable components that are executable by the plurality of processors, comprising: a data partition component that partitions training data for training deep neural networks (DNNs) into multiple data blocks that preserve joint distribution of input features and an output class of the training data; and a training component that performs an alternating direction method of multipliers (ADMM) iteration on the multiple data blocks in an initial order using multiple worker nodes to train the DNNs, the multiple worker nodes including a plurality of central processing units or a plurality of graphics processing units, and determines that the DNNs are trained into trained DNNs in response to a stop criterion being satisfied following the ADMM iteration. 18. The system of claim 17, further comprising a validation component that determines that the stop criterion is satisfied in response to a relative change in an objective function associated with the ADMM iteration being less than a predetermined change threshold, a classification accuracy of the trained DNNs with respect to a validation data set being within a predetermined percentage of a baseline classification accuracy generated by a baseline classifier for the validation data set, or a model parameter of a global model associated with the classifier reaching a predetermined value. 19. The system of claim 18, wherein the training component further performs an additional ADMM iteration on the multiple data blocks in a different order in response to the stop criterion being unsatisfied following the ADMM iteration. 20. The system of claim 17, wherein the training component performs the ADMM iteration by: partitioning a data block of the multiple data blocks into multiple data subsets; distributing the multiple data subsets and a global model to the multiple worker nodes, each of the worker nodes having a local model; receiving updated local models corresponding to the multiple data subsets from the multiple worker nodes, each updated local model being updated independently at a corresponding worker node using a single-sweep mini-batch based stochastic gradient descent processing of a corresponding data subset; and aggregating the updated local models to update the global model.