- 'mendeley.bib'
title:
- 'Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer Vision'
- Supplementary Material
---

Introduction {#sec:intro}
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Thanks to recent advances in computing, deep learning based systems, which employ very large numbers of inputs, have been developed in the last decade. However, processing/labeling/communication of a large number of input data has remained challenging. Therefore, novel machine learning algorithms that make the best use of a significantly less amount of data are of great interest. For example, active learning (AL) [@lewis1994sequential] aims at addressing this problem by iteratively training a model using a small number of labeled data, testing the whole data on the trained model, and then querying the labels of some selected data, which then are used for training a new model. In this context, preserving the underlying structure of data by a succinct format is an essential concern. Data selection task is not trivial and possibly implies addressing an NP-hard problem (i.e., there are $M\choose K$ possibilities of choosing $K$ distinct sample out of $M$ available ones). This means that an optimal solution cannot be efficiently computed when the number of available data becomes excessively large. A convex relaxation of the 