different selection methods for different numbers of representatives per class. As more samples are collected, the performance gap among different methods, including random, decreases. This is expected, since finding only one representative for each class is a much more difficult task, compared to choosing many, e.g. $6$, representatives.

Using only one representative selected by IPM, we can achieve a classification accuracy of 65.3%, which is more than 10% improvement compared to random selection and more than 4% improvement compared to other competitors.

[0.25]{} ![t-SNE visualization [@LaurensvanderMaaten2014VisualizingT-SNE] of two randomly selected classes of UCF-101 dataset and their representatives selected by different methods. () Decision function learned by using all the data. The goal of selection is to preserve the structure with only a few representatives. () Decision function learned by using 2 (first column), 5 (second column), and 10 (third column) representatives per class, using K-medoids (first row), DS3 [@Elhamifar2016DissimilaritySelection] (second row), and IPM (third row). IPM can capture the structure of the data better using the same number of selected samples.[]{data-label="fig:tsne_ucf101"}](Figures/original.eps "fig:"){width="1\columnwidth"}

[0.6]{} ![t-SNE visualization [@LaurensvanderMaaten2014VisualizingT-SNE] of two randomly selected classes of UCF-101 dataset and their representatives selected by different methods. () Decision function learned by using all the data. 