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• W2019901698 abstract "In this paper, we consider a technique called the generic Principal Component Analysis (PCA) which is based on an extension and rigorous justification of the standard PCA. The generic PCA is treated as the best weighted linear estimator of a given rank under the condition that the associated covariance matrix is singular. As a result, the generic PCA is constructed in terms of the pseudo-inverse matrices that imply a development of the special technique. In particular, we give a solution of the new low-rank matrix approximation problem that provides a basis for the generic PCA. Theoretical aspects of the generic PCA are carefully studied." @default.
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• W2019901698 date "2009-04-01" @default.
• W2019901698 modified "2023-09-23" @default.
• W2019901698 title "Towards theory of generic Principal Component Analysis" @default.
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• W2019901698 doi "https://doi.org/10.1016/j.jmva.2008.07.005" @default.
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