Multiresolution using principal component analysis

This paper proposes principal component analysis (PCA) to find adaptive bases for multiresolution. An input image is decomposed into components (compressed images) which are uncorrelated and have maximum l/sub 2/ energy. With only minor modification, a single layer linear network using the generalized Hebbian algorithm (GHA) is used for multiresolution PCA. The decomposition has been successfully applied to face classification. Good results with biological signals have also been reported.

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