Presentation 2013-09-02
Optimization for Robust Support Vector Machine Utilizing Homotopy Approach
Shinya SUZUMURA, Kohei OGAWA, Ichiro TAKEUCHI, Masashi SUGIYAMA,
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Abstract(in English) In this paper, we propose a novel optimization method for robust SVM that has robustness to outliers. In robust SVM, it is important to adaptively determine which instances are considered as outliers, and how much the effect of outliers should be alleviated. We introduce a pair of tuning parameters that adjust the outlier's criterion and effects, and develop an algorithm that computes the path of solutions when those parameters are continuously changed. Since the loss function of robust SVM is non-convex, our proposed algorithm computes a path of local optimal solutions. Unlike solution paths of convex problems, our local optimal solution path are shown to have finite number of discrete points. Our algorithm can identify those discrete points and properly handle those discontinuities in the path computation. We illustrate the effectiveness of our algorithm through numerical experiments.
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Keyword(in English) Robust Support Vector Machine / Homotopy Approach / Annealing
Paper # PRMU2013-36,IBISML2013-16
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Committee PRMU
Conference Date 2013/8/26(1days)
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Registration To Pattern Recognition and Media Understanding (PRMU)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Optimization for Robust Support Vector Machine Utilizing Homotopy Approach
Sub Title (in English)
Keyword(1) Robust Support Vector Machine
Keyword(2) Homotopy Approach
Keyword(3) Annealing
1st Author's Name Shinya SUZUMURA
1st Author's Affiliation Nagoya Institute of Technology()
2nd Author's Name Kohei OGAWA
2nd Author's Affiliation Nagoya Institute of Technology
3rd Author's Name Ichiro TAKEUCHI
3rd Author's Affiliation Nagoya Institute of Technology
4th Author's Name Masashi SUGIYAMA
4th Author's Affiliation Tokyo Institute of Technology
Date 2013-09-02
Paper # PRMU2013-36,IBISML2013-16
Volume (vol) vol.113
Number (no) 196
Page pp.pp.-
#Pages 6
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