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

Y. Matsuyama, S. Imahara

    研究成果: Conference contribution

    1 引用 (Scopus)

    抜粋

    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.

    元の言語English
    ホスト出版物のタイトルProceedings of the International Joint Conference on Neural Networks
    ページ412-417
    ページ数6
    1
    出版物ステータスPublished - 2001
    イベントInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC
    継続期間: 2001 7 152001 7 19

    Other

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

    ASJC Scopus subject areas

    • Software

    フィンガープリント Independent component analysis by convex divergence minimization: Applications to brain fMRI analysis' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

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