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A Generalized Contrast Function and Stability Analysis for Overdetermined Blind Separation of Instantaneous Mixtures.
- Source :
-
Neural Computation . Mar2006, Vol. 18 Issue 3, p709-728. 20p. - Publication Year :
- 2006
-
Abstract
- In this letter, the problem of blind separation of n independent sources from their m linear instantaneous mixtures is considered. First, a generalized contrast function is defined as a valuable extension of the existing classical and nonsymmetrical contrast functions. It is applicable to the overdetermined blind separation (m > n) with an unknown number of sources, because not only independent components but also redundant ones are allowed in the outputs of a separation system. Second, a natural gradient learning algorithm developed primarily for the complete case (m = n) is shown to work as well with an n × m or m × m separating matrix, for each optimizes a certain mutual information contrast function. Finally, we present stability analysis for a newly proposed generalized orthogonal natural gradient algorithm (which can perform the overdetermined blind separation when n is unknown), obtaining an expectable result that its local stability conditions are slightly stricter than those of the conventional natural gradient algorithm using an invertible mixing matrix (m = n). [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08997667
- Volume :
- 18
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Neural Computation
- Publication Type :
- Academic Journal
- Accession number :
- 19636721
- Full Text :
- https://doi.org/10.1162/neco.2006.18.3.709