Independent component analysis by convex divergence minimization: Applications to brain fMRI analysis

Y. Matsuyama, S. Imahara

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

    1 Citation (Scopus)

    Abstract

    The f-ICA was derived from the minimization of the convex divergence. Software implementations showed remarkable speed as a gradient descent type. This was due to the effective use of past and/or future data.

    Original languageEnglish
    Title of host publicationProceedings of the International Joint Conference on Neural Networks
    Pages412-417
    Number of pages6
    Volume1
    Publication statusPublished - 2001
    EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC
    Duration: 2001 Jul 152001 Jul 19

    Other

    OtherInternational Joint Conference on Neural Networks (IJCNN'01)
    CityWashington, DC
    Period01/7/1501/7/19

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    ASJC Scopus subject areas

    • Software

    Cite this

    Matsuyama, Y., & Imahara, S. (2001). Independent component analysis by convex divergence minimization: Applications to brain fMRI analysis. In Proceedings of the International Joint Conference on Neural Networks (Vol. 1, pp. 412-417)