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• W2004697122 abstract "In this paper, we review recent advances in blind source separation (BSS) and independent component analysis (ICA) for nonlinear mixing models. After a general introduction to BSS and ICA, we discuss in more detail uniqueness and separability issues, presenting some new results. A fundamental difficulty in the nonlinear BSS problem and even more so in the nonlinear ICA problem is that they provide non-unique solutions without extra constraints, which are often implemented by using a suitable regularization. In this paper, we explore two possible approaches. The first one is based on structural constraints. Especially, post-nonlinear mixtures are an important special case, where a nonlinearity is applied to linear mixtures. For such mixtures, the ambiguities are essentially the same as for the linear ICA or BSS problems. The second approach uses Bayesian inference methods for estimating the best statistical parameters, under almost unconstrained models in which priors can be easily added. In the later part of this paper, various separation techniques proposed for post-nonlinear mixtures and general nonlinear mixtures are reviewed." @default.
• W2004697122 created "2016-06-24" @default.
• W2004697122 creator A5055562421 @default.
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• W2004697122 date "2004-10-01" @default.
• W2004697122 modified "2023-10-14" @default.
• W2004697122 title "ADVANCES IN BLIND SOURCE SEPARATION (BSS) AND INDEPENDENT COMPONENT ANALYSIS (ICA) FOR NONLINEAR MIXTURES" @default.
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• W2004697122 doi "https://doi.org/10.1142/s012906570400208x" @default.
• W2004697122 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/15593377" @default.
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