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Selection of number of principal components for de-noising signals

Koutsogiannis, G. and Soraghan, J.J. (2002) Selection of number of principal components for de-noising signals. Electronics Letters, 38 (13). pp. 664-666. ISSN 0013-5194

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Principal component analysis (PCA) is a transformation technique used to reduce the dimensionality of a dataset. Using delay embedding, it is possible to know a priori how many principal components to choose to obtain the optimum reconstruction. A novel nonlinear PCA-based scheme employing delay embedding is presented for the de-noising of communication signals.