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

Ryota Yokote, Toshikazu Nakamura, Yasuo Matsuyama

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

    Abstract

    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.

    Original languageEnglish
    Title of host publicationProceedings of the International Joint Conference on Neural Networks
    Pages701-708
    Number of pages8
    DOIs
    Publication statusPublished - 2011
    Event2011 International Joint Conference on Neural Network, IJCNN 2011 - San Jose, CA
    Duration: 2011 Jul 312011 Aug 5

    Other

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

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    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence

    Cite this

    Yokote, R., Nakamura, T., & Matsuyama, Y. (2011). Independent component analysis with graphical correlation: Applications to multi-vision coding. In Proceedings of the International Joint Conference on Neural Networks (pp. 701-708). [6033290] https://doi.org/10.1109/IJCNN.2011.6033290