@techreport{oai:ipsj.ixsq.nii.ac.jp:00164512,
 author = {柴垣, 篤志 and 烏山, 昌幸 and 畑埜, 晃平 and 竹内, 一郎 and Atsushi, Shibagaki and Masayuki, Karasuyama and Kohei, Hatano and Ichiro, Takeuchi},
 issue = {39},
 month = {Jun},
 note = {スパースモデルの学習は，概念的にはアクティブな特徴/標本を特定するプロセスとそれらに対応する変数を最適化するプロセスに分けられる．近年セーフスクリーニングと呼ばれる一部の非アクティブな特徴/標本を同定する手法は特徴/標本それぞれで独立して研究されてきた．本論文では特徴と標本のセーフスクリーニングを交互に繰り返し行い，同時にセーフスクリーニングすることによる相乗効果を考えることによって独立に行うよりも強力なセーフスクリーニング手法を提案する．, 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.},
 title = {スパースモデルのための特徴と標本の同時セーフスクリーニング},
 year = {2016}
}