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Principal Component Analysis (PCA) is concerned basically with finding an optimal way to represent a random vector through a linear combination of a few uncorrelated random variables. In signal processing, multiresolution transforms are used to decompose a time signal into components of different resolutions. In this paper, we consider designing optimal multiresolution transforms such that components in each resolution provide the best approximation to the original signal in that resolution. Wedoi:10.1109/ijcnn.1999.831076 dblp:conf/ijcnn/JahromiF99 fatcat:52sk75zxp5cmbbwhvprh5nnz4u