Multi-task feature selection on multiple networks via maximum flows

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

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

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationSIAM International Conference on Data Mining 2014, SDM 2014
EditorsMohammed J. Zaki, Arindam Banerjee, Srinivasan Parthasarathy, Pang Ning-Tan, Zoran Obradovic, Chandrika Kamath
PublisherSociety for Industrial and Applied Mathematics Publications
Pages199-207
Number of pages9
ISBN (Electronic)9781510811515
DOIs
Publication statusPublished - 2014
Event14th SIAM International Conference on Data Mining, SDM 2014 - Philadelphia, United States
Duration: Apr 24 2014Apr 26 2014

Publication series

NameSIAM International Conference on Data Mining 2014, SDM 2014
Volume1

Conference

Conference14th SIAM International Conference on Data Mining, SDM 2014
CountryUnited States
CityPhiladelphia
Period4/24/144/26/14

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

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  • Cite this

    Sugiyama, M., Azencott, C. A., Grimm, D., Kawahara, Y., & Borgwardt, K. M. (2014). Multi-task feature selection on multiple networks via maximum flows. In M. J. Zaki, A. Banerjee, S. Parthasarathy, P. Ning-Tan, Z. Obradovic, & C. Kamath (Eds.), SIAM International Conference on Data Mining 2014, SDM 2014 (pp. 199-207). (SIAM International Conference on Data Mining 2014, SDM 2014; Vol. 1). Society for Industrial and Applied Mathematics Publications. https://doi.org/10.1137/1.9781611973440.23