SOMDS: Multidimensional scaling through self organization map

Kempei Shiina, S. Asakawa

    研究成果: Conference contribution

    抜粋

    We propose SOMDS that is a combination of MDS (multidimensional scaling) and SOM. SOMDS is a special type of MDS that can learn locally and adaptively the structure of similarity data. SOMDS is a special type of SOM without neighborhood functions and whose inputs are similarities between objects. Convergence properties of the algorithm and some applications are presented.

    元の言語English
    ホスト出版物のタイトルICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
    出版者Institute of Electrical and Electronics Engineers Inc.
    ページ2579-2581
    ページ数3
    5
    ISBN(電子版)9810475241, 9789810475246
    DOI
    出版物ステータスPublished - 2002
    イベント9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore
    継続期間: 2002 11 182002 11 22

    Other

    Other9th International Conference on Neural Information Processing, ICONIP 2002
    Singapore
    Singapore
    期間02/11/1802/11/22

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Information Systems
    • Signal Processing

    これを引用

    Shiina, K., & Asakawa, S. (2002). SOMDS: Multidimensional scaling through self organization map. : ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age (巻 5, pp. 2579-2581). [1201961] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICONIP.2002.1201961