Distance metric learning based on different ℓ1 regularized metric matrices in each category

Kenta Mikawa, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

    研究成果: Conference contribution

    抜粋

    The distance metric learning is the method to learn the distance metric from training data considering its statistical characteristics under the arbitrary constraints. To obtain the desirable distance metric, the optimization problem is solved. Most of the distance metric learning methods aim to gain the global optimal metric matrix. However there is a possibility that the global metric matrix cannot express the statistical characteristics of each category in detail. In addition, if the dimension of input data increase, the computational cost of calculating distance between data increases either. To avoid this problem, we adopt the way to use the l1 regularization to gain sparse metric matrix. By combining those, we focus on the way to deriving the plural metric matrices with a sparse structure in this study. To verify the effective ness of our proposed method, we conduct simulation experiments by using UCI machine learning repository.

    元の言語English
    ホスト出版物のタイトルProceedings of 2016 International Symposium on Information Theory and Its Applications, ISITA 2016
    出版者Institute of Electrical and Electronics Engineers Inc.
    ページ285-289
    ページ数5
    ISBN(電子版)9784885523090
    出版物ステータスPublished - 2017 2 2
    イベント3rd International Symposium on Information Theory and Its Applications, ISITA 2016 - Monterey, United States
    継続期間: 2016 10 302016 11 2

    Other

    Other3rd International Symposium on Information Theory and Its Applications, ISITA 2016
    United States
    Monterey
    期間16/10/3016/11/2

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Hardware and Architecture
    • Information Systems
    • Signal Processing
    • Library and Information Sciences

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  • これを引用

    Mikawa, K., Kobayashi, M., Goto, M., & Hirasawa, S. (2017). Distance metric learning based on different ℓ1 regularized metric matrices in each category. : Proceedings of 2016 International Symposium on Information Theory and Its Applications, ISITA 2016 (pp. 285-289). [7840431] Institute of Electrical and Electronics Engineers Inc..