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Multivariate Denoising Using Wavelets and Principal Component Analysis

Abstract : A multivariate extension of the well known wavelet denoising procedure widely examined for scalar valued signals, is proposed. It combines a straightforward multivariate generalization of a classical one and principal component analysis. This new procedure exhibits promising behavior on classical bench signals and the associated estimator is found to be near minimax in the one-dimensional sense, for Besov balls. The method is finally illustrated by an application to multichannel neural recordings.
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https://hal-univ-paris10.archives-ouvertes.fr/hal-01633702
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Submitted on : Monday, November 13, 2017 - 11:58:35 AM
Last modification on : Wednesday, September 16, 2020 - 4:05:36 PM

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Mina Aminghafari, Nathalie Chèze, Jean-Michel Poggi. Multivariate Denoising Using Wavelets and Principal Component Analysis. Computational Statistics and Data Analysis, Elsevier, 2006, Statistical signal extraction and filtering, 50 (9), pp.2381--2398. ⟨10.1016/j.csda.2004.12.010⟩. ⟨hal-01633702⟩

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