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Denoising using local projective subspace methods

Gruber, Peter ; Stadlthanner, Kurt ; Böhm, M. ; Theis, Fabian J. ; Tomé, A. M. ; Teixeira, A. R. ; Puntonet, Carlos G. ; Gorriz, J. M. ; Lang, Elmar



Abstract

In this paper we present previous termdenoisingnext term algorithms for enhancing noisy signals based on previous termLocalnext term ICA (LICA), Delayed AMUSE (dAMUSE) and Kernel PCA (KPCA). The algorithm LICA relies on applying ICA locally to clusters of signals embedded in a high-dimensional feature space of delayed coordinates. The components resembling the signals can be detected by various ...

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