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Estimator for Number of Sources Using Minimum Description Length Criterion for Blind Sparse Source Mixtures

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Independent Component Analysis and Signal Separation (ICA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4666))

Abstract

In this paper I present a Minimum Description Length Estimator for number of sources in an anechoic mixture of sparse signals. The criterion is roughly equal to the sum of negative normalized maximum log-likelihood and the logarithm of number of sources. Numerical evidence supports this approach and compares favorabily to both the Akaike (AIC) and Bayesian (BIC) Information Criteria.

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Authors and Affiliations

  1. Siemens Corporate Research, 755 College Road East, Princeton, NJ 08540,  

    Radu Balan

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  1. Radu Balan
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Editor information

Mike E. Davies Christopher J. James Samer A. Abdallah Mark D Plumbley

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© 2007 Springer-Verlag Berlin Heidelberg

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Balan, R. (2007). Estimator for Number of Sources Using Minimum Description Length Criterion for Blind Sparse Source Mixtures. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_42

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  • DOI: https://doi.org/10.1007/978-3-540-74494-8_42

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74493-1

  • Online ISBN: 978-3-540-74494-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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