Independent component analysis with graphical correlation: Applications to multi-vision coding

Ryota Yokote*, Toshikazu Nakamura, Yasuo Matsuyama

*この研究の対応する著者

    研究成果: Conference contribution

    抄録

    New algorithms for joint learning of independent component analysis and graphical high-order correlation (GC-ICA: Graphically Correlated ICA) are presented. The presented method has a fixed point style or of the FastICA, however, it comprises independent but correlated subparts. Correlations by teacher signals are also allowed. In spite of such inclusion of the dependency, the presented algorithm shows fast convergence. The converged set of bases has reduced indeterminacy on the ordering. This is equivalent to a self-organization of bases. This method can be used to analyze multiple images simultaneously. Examples are given on images from 3D- stereo videos shots. The correlation of bases on left and right eye views is shown for the first time here. Further speedup using the strategy of the RapidICA is possible.

    本文言語English
    ホスト出版物のタイトルProceedings of the International Joint Conference on Neural Networks
    ページ701-708
    ページ数8
    DOI
    出版ステータスPublished - 2011
    イベント2011 International Joint Conference on Neural Network, IJCNN 2011 - San Jose, CA
    継続期間: 2011 7月 312011 8月 5

    Other

    Other2011 International Joint Conference on Neural Network, IJCNN 2011
    CitySan Jose, CA
    Period11/7/3111/8/5

    ASJC Scopus subject areas

    • ソフトウェア
    • 人工知能

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