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Denoising using local ICA and a generalized eigendecomposition with time-delayed signals

Gruber, P. ; Stadlthanner, K. ; Tomé, A. ; Teixeira, A. ; Theis, Fabian J. ; Puntonet, Carlos G. ; Lang, Elmar



Abstract

We present denoising algorithms based on either local independent component analysis (ICA) and a minimum description length (MDL) estimator or a generalized eigenvalue decomposition (GEVD) using a matrix pencil of time-delayed signals. Both methods are applied to signals embedded in delayed coordinates in a high-dim feature space OHgr and denoising is achieved by projecting onto a lower ...

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