  The goal of data selection is to capture the most structural information from
a set of data. This paper presents a fast and accurate data selection method,
in which the selected samples are optimized to span the subspace of all data.
We propose a new selection algorithm, referred to as iterative projection and
matching (IPM), with linear complexity w.r.t. the number of data, and without
any parameter to be tuned. In our algorithm, at each iteration, the maximum
information from the structure of the data is captured by one selected sample,
and the captured information is neglected in the next iterations by projection
on the null-space of previously selected samples. The computational efficiency
and the selection accuracy of our proposed algorithm outperform those of the
conventional methods. Furthermore, the superiority of the proposed algorithm is
shown on active learning for video action recognition dataset on UCF-101;
learning using representatives on ImageNet; training a generative adversarial
network (GAN) to generate multi-view images from a single-view input on CMU
Multi-PIE dataset; and video summarization on UTE Egocentric dataset.
