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

Kenta Mikawa, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

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

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1985-1990
Number of pages6
EditionJanuary
ISBN (Electronic)9781479938407
DOIs
Publication statusPublished - 2014 Jan 1
Event2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 - San Diego, United States
Duration: 2014 Oct 52014 Oct 8

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
NumberJanuary
Volume2014-January
ISSN (Print)1062-922X

Other

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

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction

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    Mikawa, K., Kobayashi, M., Goto, M., & Hirasawa, S. (2014). A proposal of l1 regularized distance metric learning for high dimensional sparse vector space. In Proceedings - 2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 (January ed., pp. 1985-1990). [6974212] (Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics; Vol. 2014-January, No. January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/smc.2014.6974212