Multi-task feature selection on multiple networks via maximum flows

Mahito Sugiyama, Chloé Agathe Azencott, Dominik Grimm, Yoshinobu Kawahara, Karsten M. Borgwardt

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

9 被引用数 (Scopus)

抄録

We propose a new formulation of multi-task feature selection coupled with multiple network regularizers, and show that the problem can be exactly and efficiently solved by maximum flow algorithms. This method contributes to one of the central topics in data mining: How to exploit structural information in multivariate data analysis, which has numerous applications, such as gene regulatory and social network analysis. On simulated data, we show that the proposed method leads to higher accuracy in discovering causal features by solving multiple tasks simultaneously using networks over features. Moreover, we apply the method to multi-locus association mapping with Arabidopsis thaliana genotypes and flowering time phenotypes, and demonstrate its ability to recover more known phenotype-related genes than other state-of-the-art methods.

本文言語英語
ホスト出版物のタイトルSIAM International Conference on Data Mining 2014, SDM 2014
編集者Mohammed J. Zaki, Arindam Banerjee, Srinivasan Parthasarathy, Pang Ning-Tan, Zoran Obradovic, Chandrika Kamath
出版社Society for Industrial and Applied Mathematics Publications
ページ199-207
ページ数9
ISBN(電子版)9781510811515
DOI
出版ステータス出版済み - 2014
外部発表はい
イベント14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, 米国
継続期間: 4月 24 20144月 26 2014

出版物シリーズ

名前SIAM International Conference on Data Mining 2014, SDM 2014
1

会議

会議14th SIAM International Conference on Data Mining, SDM 2014
国/地域米国
CityPhiladelphia
Period4/24/144/26/14

!!!All Science Journal Classification (ASJC) codes

  • コンピュータ サイエンスの応用
  • ソフトウェア

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