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/selections.eps "fig:"){width="1\columnwidth"}

Figure \[fig:tsne\_ucf101\] shows the t-SNE visualization [@LaurensvanderMaaten2014VisualizingT-SNE] of the selection process for two randomly selected classes of UCF-101. Selection is performed on the original $512$-dimensional feature space. To visualize the structure of the data, the contours represent the decision function of an SVM trained in this 2D space. This experiment illustrates that each IPM sample contains new structural information, as the selected samples are far away from each other in the t-SNE space, compared to other methods. Moreover, it is evident that as we collect more samples, the structure of the data is better captured by the samples selected by IPM, compared to other methods selecting the same number of representatives. The decision boundaries of the classifier trained on $5$ IPM-selected samples look very similar to the boundaries learned from all the data. This leads to significant accuracy 