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

Kenta Mikawa, Manabu Kobayashi, Masayuki Goto, Shigeichi Hirasawa

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

    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.

    Original languageEnglish
    Title of host publicationProceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1645-1650
    Number of pages6
    ISBN (Print)9781479986965
    DOIs
    Publication statusPublished - 2016 Jan 12
    EventIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 - Kowloon Tong, Hong Kong
    Duration: 2015 Oct 92015 Oct 12

    Other

    OtherIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
    CountryHong Kong
    CityKowloon Tong
    Period15/10/915/10/12

    Keywords

    • Distance Metric Learning
    • Pattern Recognition
    • Regularization
    • 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

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