Many information processing problems can be transformed into some form of eigenvalue or singular value problems. Eigenvalue\r\ndecomposition (EVD) and singular value decomposition (SVD) are usually used for solving these problems. In this paper, we\r\ngive an introduction to various neural network implementations and algorithms for principal component analysis (PCA) and\r\nits various extensions. PCA is a statistical method that is directly related to EVD and SVD. Minor component analysis (MCA)\r\nis a variant of PCA, which is useful for solving total least squares (TLSs) problems. The algorithms are typical unsupervised\r\nlearning methods. Some other neural network models for feature extraction, such as localized methods, complex-domain methods,\r\ngeneralized EVD, and SVD, are also described. Topics associated with PCA, such as independent component analysis (ICA) and\r\nlinear discriminant analysis (LDA), are mentioned in passing in the conclusion. These methods are useful in adaptive signal\r\nprocessing, blind signal separation (BSS), pattern recognition, and information compression.
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