Simultaneous Safe Screening of Features and Samples in Doubly Sparse Modeling


Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano, Ichiro Takeuchi ;
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:1577-1586, 2016.


The problem of learning a sparse model is conceptually interpreted as the process of identifying active features/samples and then optimizing the model over them. Recently introduced safe screening allows us to identify a part of non-active features/samples. So far, safe screening has been individually studied either for feature screening or for sample screening. In this paper, we introduce a new approach for safely screening features and samples simultaneously by alternatively iterating feature and sample screening steps. A significant advantage of considering them simultaneously rather than individually is that they have a synergy effect in the sense that the results of the previous safe feature screening can be exploited for improving the next safe sample screening performances, and vice-versa. We first theoretically investigate the synergy effect, and then illustrate the practical advantage through intensive numerical experiments for problems with large numbers of features and samples.

Related Material