Entire Regularization Path for Sparse Nonnegative Interaction Model

Mirai Takayanagi, Yasuo Tabei, Hiroto Saigo

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

Building sparse combinatorial model with non-negative constraint is essential in solving real-world problems such as in biology, in which the target response is often formulated by additive linear combination of features variables. This paper presents a solution to this problem by combining itemset mining with non-negative least squares. However, once incorporation of modern regularization is considered, then a naive solution requires to solve expensive enumeration problem many times for every regularization parameter. In this paper, we devise a regularization path tracking algorithm such that combinatorial feature is searched and included one by one to the solution set. Our contribution is a proposal of novel bounds specifically designed for the feature search problem. In synthetic dataset, the proposed method is demonstrated to run orders of magnitudes faster than a naive counterpart which does not employ tree pruning. We also empirically show that non-negativity constraints can reduce the number of active features much less than that of LASSO, leading to significant speed-ups in pattern search. In experiments using HIV-1 drug resistance dataset, the proposed method could successfully model the rapidly increasing drug resistance triggered by accumulation of mutations in HIV-1 genetic sequences. We also demonstrate the effectiveness of non-negativity constraints in suppressing false positive features, resulting in a model with smaller number of features and thereby improved interpretability.

本文言語英語
ホスト出版物のタイトル2018 IEEE International Conference on Data Mining, ICDM 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1254-1259
ページ数6
ISBN(電子版)9781538691588
DOI
出版ステータス出版済み - 12 27 2018
イベント18th IEEE International Conference on Data Mining, ICDM 2018 - Singapore, シンガポール
継続期間: 11 17 201811 20 2018

出版物シリーズ

名前Proceedings - IEEE International Conference on Data Mining, ICDM
2018-November
ISSN(印刷版)1550-4786

会議

会議18th IEEE International Conference on Data Mining, ICDM 2018
Countryシンガポール
CitySingapore
Period11/17/1811/20/18

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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