TY - GEN
T1 - Simultaneous safe screening of features and samples in doubly sparse modeling
AU - Shibagaki, Atsushi
AU - Karasuyama, Masayuki
AU - Hatano, Kohei
AU - Takeuchi, Ichiro
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84998693042&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84998693042&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84998693042
T3 - 33rd International Conference on Machine Learning, ICML 2016
SP - 2381
EP - 2390
BT - 33rd International Conference on Machine Learning, ICML 2016
A2 - Weinberger, Kilian Q.
A2 - Balcan, Maria Florina
PB - International Machine Learning Society (IMLS)
T2 - 33rd International Conference on Machine Learning, ICML 2016
Y2 - 19 June 2016 through 24 June 2016
ER -