In this work, we deal with the problem of nonlinear blind source separation (BSS). We propose a new method for BSS in overdetermined linear-quadratic (LQ) mixtures. By exploiting the assumption that the sources are sparse in a transformed domain, we define a framework for canceling the nonlinear part of the mixing process. After that, separation can be conducted by linear BSS algorithms. Experiments with synthetic data are performed to assess the viability of our proposal. © 2012 Springer-Verlag.
CITATION STYLE
Duarte, L. T., Ando, R. A., Attux, R., Deville, Y., & Jutten, C. (2012). Separation of sparse signals in overdetermined linear-quadratic mixtures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7191 LNCS, pp. 239–246). https://doi.org/10.1007/978-3-642-28551-6_30
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