Xu, Lei; Cheung, Chi Chiu; Amari, Shun-ichi Learned parametric mixture based ICA algorithm. (English) Zbl 0911.68159 Neurocomputing 22, No. 1-3, 69-80 (1998). Summary: The learned parametric mixture method is presented for a canonical cost function based ICA model on linear mixture, with several new findings. First, its adaptive algorithm is further refined into a simple concise form. Second, the separation ability of this method is shown to be qualitatively superior to its original model with prefixed nonlinearity. Third, a heuristic way is suggested for selecting the number of densities in a learned parametric mixture. Finally, experiments have been conducted to show the success of this method on the sources that can either be sub-Gaussian or super-Gaussian, as well as a combination of both the types. Cited in 1 ReviewCited in 7 Documents MSC: 68T05 Learning and adaptive systems in artificial intelligence 68W10 Parallel algorithms in computer science Keywords:parametric mixture method; ICA model PDFBibTeX XMLCite \textit{L. Xu} et al., Neurocomputing 22, No. 1--3, 69--80 (1998; Zbl 0911.68159) Full Text: DOI Link