PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Caiafa C., Kuruoglu E. E. Using generic order moments for separation of dependent sources with linear conditional expectations. In: EUSIPCO - 21st European Signal Processing Conference (Marrakesh, Morocco, 9-13 September 2013). Proceedings, article n. Th-P1.13. EURASIP, 2013.
 
 
Abstract
(English)
n this work, we approach the blind separation of dependent sources based only on a set oftheirlinearmixtures. We prove that, when the sources have a pairwise dependence characterized by the linear conditional expectation (LCE) law, i.e. E[Si|Sj]=ρijSj for i = j, with ρij=E[SiSj](correlation coefficient), we are able to separate them by maximizing or minimizing a Generic Order Moment (GOM) of their mixture defined by μp=E[|α1S1+α2S2|^p]. This general measure includes the higher order as well as the fractional moment cases. Our results, not only confirm some of the existing results for the independent sources case but also they allow us to explore new objective functions for Dependent Component Analysis. A set of examples illustrating the consequences of our theory is presented. Also, a comparison of our GOM based algorithm, the classical FAST ICA and a very recently proposed algorithm for dependent sources, the Bounded Component Analysis (BCA) algorithm, is shown.
URL: http://www.eurasip.org/Proceedings/Eusipco/Eusipco2013/index.html
Subject Source separation
Dependent component analysis
Method of moments
Fractional order moments
G.3 PROBABILITY AND STATISTICS. Multivariate statistics
62H25 Factor analysis and principal components; correspondence analysis


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