A proposal of l1 regularized distance metric learning for high dimensional sparse vector space

Kenta Mikawa, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In this paper, we focus on pattern recognition based on the vector space model with the high dimensional and sparse data. One of the pattern recognition methods is metric learning which learns a metric matrix by using the iterative optimization procedure. However most of the metric learning methods tend to cause overfitting and increasing computational time for high dimensional and sparse settings. To avoid these problems, we propose the method of l1 regularized metric learning by using the algorithm of alternating direction method of multiplier (ADMM) in the supervised setting. The effectiveness of our proposed method is clarified by classification experiments by using the Japanese newspaper article and UCI machine learning repository. And we show proposed method is the special case of the statistical sparse covariance selection.

本文言語English
ホスト出版物のタイトルProceedings - 2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1985-1990
ページ数6
January
ISBN(電子版)9781479938407
DOI
出版ステータスPublished - 2014
イベント2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 - San Diego, United States
継続期間: 2014 10 52014 10 8

出版物シリーズ

名前Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
番号January
2014-January
ISSN(印刷版)1062-922X

Other

Other2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014
国/地域United States
CitySan Diego
Period14/10/514/10/8

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 制御およびシステム工学
  • 人間とコンピュータの相互作用

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