DocumentCode
2042443
Title
Information theory based estimator of the number of sources in a sparse linear mixing model
Author
Balan, Radu
Author_Institution
Dept. of Math., Maryland Univ., College Park, MD
fYear
2008
fDate
19-21 March 2008
Firstpage
269
Lastpage
273
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;
fLanguage
English
Publisher
ieee
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
Type
conf
DOI
10.1109/CISS.2008.4558534
Filename
4558534
Link To Document