Title :
Information theory based estimator of the number of sources in a sparse linear mixing model
Author_Institution :
Dept. of Math., Maryland Univ., College Park, MD
Abstract :
In this paper we present an Information theoretic estimator for the number of sources mutually disjoint in a linear mixing model. The approach follows the Minimum Description Length prescription and is roughly equal to the sum of negative normalized maximum log-likelihood and the logarithm of number of sources. Preliminary numerical evidence supports this approach and compares favorably to both the Akaike (AIC) and Bayesian (BIC) Information Criteria.
Keywords :
Bayes methods; blind source separation; maximum likelihood estimation; sparse matrices; Akaike information criteria; Bayesian information criteria; blind source separation; information theoretic estimator; minimum description length prescription; negative normalized maximum log-likelihood estimation; sparse linear mixing model; Estimation theory; Hydrogen; Information theory; Mathematical model; Mathematics; Maximum likelihood estimation; Signal analysis; Statistics; Time frequency analysis; Vectors;
Conference_Titel :
Information Sciences and Systems, 2008. CISS 2008. 42nd Annual Conference on
Conference_Location :
Princeton, NJ
Print_ISBN :
978-1-4244-2246-3
Electronic_ISBN :
978-1-4244-2247-0
DOI :
10.1109/CISS.2008.4558534
