A Joint Second-Order Statistics and Density Matching-Based Approach for Separation of Post-Nonlinear Mixtures

Abstract : In the context of Post-Nonlinear (PNL) mixtures, source separation can be performed in a two-stage approach, which encompasses a nonlinear and a linear compensation part. In the former part, however , it is usually required the knowledge of the distribution type for all sources, what may be difficult to attend. In view of this, in this work, we propose a less restrictive approach, in which it is required the knowledge of a single source distribution – here, chosen to be a colored Gaussian. The other sources are only required to present a time structure. The method combines, in a joint-based approach, the use of the second-order statistics (SOS) and the matching of distributions, which shows to be less costly than the classical method of computing the marginal entropy for all sources. The simulation results are favorable to the proposal.
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Submitted on : Tuesday, February 28, 2017 - 6:35:47 PM
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Denis Fantinato, Leonardo Duarte, Paolo Zanini, Bertrand Rivet, Romis Attux, et al.. A Joint Second-Order Statistics and Density Matching-Based Approach for Separation of Post-Nonlinear Mixtures. 13th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA 2017), Olivier Michel; Nadège Thirion-Moreau, Feb 2017, Grenoble, France. pp.499 - 508, ⟨10.1007/978-3-319-53547-0_47⟩. ⟨hal-01479431⟩

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