SOMDS: Multidimensional scaling through self organization map

Kempei Shiina, S. Asakawa

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

    Abstract

    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.

    Original languageEnglish
    Title of host publicationICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2579-2581
    Number of pages3
    Volume5
    ISBN (Electronic)9810475241, 9789810475246
    DOIs
    Publication statusPublished - 2002
    Event9th International Conference on Neural Information Processing, ICONIP 2002 - Singapore, Singapore
    Duration: 2002 Nov 182002 Nov 22

    Other

    Other9th International Conference on Neural Information Processing, ICONIP 2002
    CountrySingapore
    CitySingapore
    Period02/11/1802/11/22

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Information Systems
    • Signal Processing

    Cite this

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