Multivariate Denoising Using Wavelets and Principal Component Analysis

Mina Aminghafari (1) , Nathalie Chèze (2) , Jean-Michel Poggi (3)

1 AUT - AmiKabir University of Technology
2 MODAL'X - Modélisation aléatoire de Paris X
3 LM-Orsay - Laboratoire de Mathématiques d'Orsay

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.

Domaines: Statistiques [math.ST]

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