Simultaneous safe screening of features and samples in doubly sparse modeling

Atsushi Shibagaki, Masayuki Karasuyama, Kohei Hatano, Ichiro Takeuchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Citations (Scopus)


The problem of learning a sparse model is conceptually interpreted as the process of identify-ing 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 al-ternatively 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.

Original languageEnglish
Title of host publication33rd International Conference on Machine Learning, ICML 2016
EditorsKilian Q. Weinberger, Maria Florina Balcan
PublisherInternational Machine Learning Society (IMLS)
Number of pages10
ISBN (Electronic)9781510829008
Publication statusPublished - 2016
Event33rd International Conference on Machine Learning, ICML 2016 - New York City, United States
Duration: Jun 19 2016Jun 24 2016

Publication series

Name33rd International Conference on Machine Learning, ICML 2016


Other33rd International Conference on Machine Learning, ICML 2016
Country/TerritoryUnited States
CityNew York City

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Computer Networks and Communications

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