A Study of Distance Metric Learning by Considering the Distances between Category Centroids

Kenta Mikawa, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

    研究成果: Conference contribution

    抄録

    In this paper, we focus on pattern recognition based on the vector space model. As one of the methods, distance metric learning is known for the learning metric matrix under the arbitrary constraint. Generally, it uses iterative optimization procedure in order to gain suitable distance structure by considering the statistical characteristics of training data. Most of the distance metric learning methods estimate suitable metric matrix from all pairs of training data. However, the computational cost is considerable if the number of training data increases in this setting. To avoid this problem, we propose the way of learning distance metric by using the each category centroid. To verify the effectiveness of proposed method, we conduct the simulation experiment by using benchmark data.

    元の言語English
    ホスト出版物のタイトルProceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
    出版者Institute of Electrical and Electronics Engineers Inc.
    ページ1645-1650
    ページ数6
    ISBN(印刷物)9781479986965
    DOI
    出版物ステータスPublished - 2016 1 12
    イベントIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 - Kowloon Tong, Hong Kong
    継続期間: 2015 10 92015 10 12

    Other

    OtherIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
    Hong Kong
    Kowloon Tong
    期間15/10/915/10/12

    Fingerprint

    Distance education
    Vector spaces
    Pattern recognition
    Costs
    Experiments
    Simulation experiment
    Learning methods
    Benchmark
    Distance learning
    Vector space model

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Networks and Communications
    • Energy Engineering and Power Technology
    • Information Systems and Management
    • Control and Systems Engineering

    これを引用

    Mikawa, K., Kobayashi, M., Goto, M., & Hirasawa, S. (2016). A Study of Distance Metric Learning by Considering the Distances between Category Centroids. : Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 (pp. 1645-1650). [7379422] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SMC.2015.290

    A Study of Distance Metric Learning by Considering the Distances between Category Centroids. / Mikawa, Kenta; Kobayashi, Manabu; Goto, Masayuki; Hirasawa, Shigeichi.

    Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1645-1650 7379422.

    研究成果: Conference contribution

    Mikawa, K, Kobayashi, M, Goto, M & Hirasawa, S 2016, A Study of Distance Metric Learning by Considering the Distances between Category Centroids. : Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015., 7379422, Institute of Electrical and Electronics Engineers Inc., pp. 1645-1650, IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015, Kowloon Tong, Hong Kong, 15/10/9. https://doi.org/10.1109/SMC.2015.290
    Mikawa K, Kobayashi M, Goto M, Hirasawa S. A Study of Distance Metric Learning by Considering the Distances between Category Centroids. : Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1645-1650. 7379422 https://doi.org/10.1109/SMC.2015.290
    Mikawa, Kenta ; Kobayashi, Manabu ; Goto, Masayuki ; Hirasawa, Shigeichi. / A Study of Distance Metric Learning by Considering the Distances between Category Centroids. Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1645-1650
    @inproceedings{f37cf29b5d534db69860566be135468e,
    title = "A Study of Distance Metric Learning by Considering the Distances between Category Centroids",
    abstract = "In this paper, we focus on pattern recognition based on the vector space model. As one of the methods, distance metric learning is known for the learning metric matrix under the arbitrary constraint. Generally, it uses iterative optimization procedure in order to gain suitable distance structure by considering the statistical characteristics of training data. Most of the distance metric learning methods estimate suitable metric matrix from all pairs of training data. However, the computational cost is considerable if the number of training data increases in this setting. To avoid this problem, we propose the way of learning distance metric by using the each category centroid. To verify the effectiveness of proposed method, we conduct the simulation experiment by using benchmark data.",
    keywords = "Distance Metric Learning, Pattern Recognition, Regularization, Vector Space Model",
    author = "Kenta Mikawa and Manabu Kobayashi and Masayuki Goto and Shigeichi Hirasawa",
    year = "2016",
    month = "1",
    day = "12",
    doi = "10.1109/SMC.2015.290",
    language = "English",
    isbn = "9781479986965",
    pages = "1645--1650",
    booktitle = "Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015",
    publisher = "Institute of Electrical and Electronics Engineers Inc.",

    }

    TY - GEN

    T1 - A Study of Distance Metric Learning by Considering the Distances between Category Centroids

    AU - Mikawa, Kenta

    AU - Kobayashi, Manabu

    AU - Goto, Masayuki

    AU - Hirasawa, Shigeichi

    PY - 2016/1/12

    Y1 - 2016/1/12

    N2 - In this paper, we focus on pattern recognition based on the vector space model. As one of the methods, distance metric learning is known for the learning metric matrix under the arbitrary constraint. Generally, it uses iterative optimization procedure in order to gain suitable distance structure by considering the statistical characteristics of training data. Most of the distance metric learning methods estimate suitable metric matrix from all pairs of training data. However, the computational cost is considerable if the number of training data increases in this setting. To avoid this problem, we propose the way of learning distance metric by using the each category centroid. To verify the effectiveness of proposed method, we conduct the simulation experiment by using benchmark data.

    AB - In this paper, we focus on pattern recognition based on the vector space model. As one of the methods, distance metric learning is known for the learning metric matrix under the arbitrary constraint. Generally, it uses iterative optimization procedure in order to gain suitable distance structure by considering the statistical characteristics of training data. Most of the distance metric learning methods estimate suitable metric matrix from all pairs of training data. However, the computational cost is considerable if the number of training data increases in this setting. To avoid this problem, we propose the way of learning distance metric by using the each category centroid. To verify the effectiveness of proposed method, we conduct the simulation experiment by using benchmark data.

    KW - Distance Metric Learning

    KW - Pattern Recognition

    KW - Regularization

    KW - Vector Space Model

    UR - http://www.scopus.com/inward/record.url?scp=84964514099&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84964514099&partnerID=8YFLogxK

    U2 - 10.1109/SMC.2015.290

    DO - 10.1109/SMC.2015.290

    M3 - Conference contribution

    AN - SCOPUS:84964514099

    SN - 9781479986965

    SP - 1645

    EP - 1650

    BT - Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015

    PB - Institute of Electrical and Electronics Engineers Inc.

    ER -