Information theory based estimator of the number of sources in a sparse linear mixing model

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.