Expectation-Maximization approaches to independent component analysis
نویسندگان
چکیده
Expectation–Maximization (EM) algorithms for independent component analysis are presented in this paper. For super-Gaussian sources, a variational method is employed to develop an EM algorithm in closed form for learning the mixing matrix and inferring the independent components. For sub-Gaussian sources, a symmetrical form of the Pearson mixture model (Neural Comput. 11 (2) (1999) 417–441) is used as the prior, which also enables the development of an EM algorithm in fclosed form for parameter estimation. r 2004 Elsevier B.V. All rights reserved.
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عنوان ژورنال:
- Neurocomputing
دوره 61 شماره
صفحات -
تاریخ انتشار 2004