Nonlinear Blind Source Separation by Integrating Independent Component Analysis and Slow Feature Analysis
نویسندگان
چکیده
In contrast to the equivalence of linear blind source separation and linear independent component analysis it is not possible to recover the original source signal from some unknown nonlinear transformations of the sources using only the independence assumption. Integrating the objectives of statistical independence and temporal slowness removes this indeterminacy leading to a new method for nonlinear blind source separation. The principle of temporal slowness is adopted from slow feature analysis, an unsupervised method to extract slowly varying features from a given observed vectorial signal. The performance of the algorithm is demonstrated on nonlinearly mixed speech data.
منابع مشابه
Independent Slow Feature Analysis and Nonlinear Blind Source Separation
In the linear case statistical independence is a sufficient criterion for performing blind source separation. In the nonlinear case, however, it leaves an ambiguity in the solutions that has to be resolved by additional criteria. Here we argue that temporal slowness complements statistical independence well and that a combination of the two leads to unique solutions of the nonlinear blind sourc...
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