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- W2017574265 abstract "The authors propose a method of obtaining a direct solution of a simultaneous low-rank approximation of multiple matrices by using a tensor–matrix product relation to derive a method of obtaining an approximate solution of an optimization problem for performing a simultaneous low-rank approximation of multiple matrices from a higher-order singular value decomposition of a tensor. Since the proposed method is a direct solution method, it is faster than a generalized low-rank approximation algorithm, which is an iterative solution method. They apply the proposed method to face image recognition to show experimentally that its recognition rate is higher and its processing is faster than the eigenface method. They also applied it to an image similarity search to show experimentally that its precision is higher than a dimensionality reduction method for simply reducing the image size. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 90(9): 42–49, 2007; Published online in Wiley InterScience (www.interscience.wiley. com). DOI 10.1002/ecjb.20379" @default.
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- W2017574265 date "2007-01-01" @default.
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- W2017574265 title "Dimensionality reduction by simultaneous low-rank approximation of matrix data" @default.
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- W2017574265 doi "https://doi.org/10.1002/ecjb.20379" @default.
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