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 Ω 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. © Springer-Verlag 2004.
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CITATION STYLE
Gruber, P., Stadlthanner, K., Tomé, A. M., Teixeira, A. R., Theis, F. J., Puntonet, C. G., & Lang, E. W. (2004). Denoising using local ICA and a generalized eigendecomposition with time-delayed signals. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3195, 993–1000. https://doi.org/10.1007/978-3-540-30110-3_125
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