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

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 dimensional signal subspace. We discuss the algorithms and provide applications to the analysis of 2D NOESY protein NMR spectra.