A map approach for ℓq-norm regularized sparse parameter estimation using the em algorithm

Rodrigo Carvajal, Juan C. Aguero, Boris I. Godoy, Dimitrios Katselis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, Bayesian parameter estimation through the consideration of the Maximum A Posteriori (MAP) criterion is revisited under the prism of the Expectation-Maximization (EM) algorithm. By incorporating a sparsity-promoting penalty term in the cost function of the estimation problem through the use of an appropriate prior distribution, we show how the EM algorithm can be used to efficiently solve the corresponding optimization problem. To this end, we rely on variance-mean Gaussian mixtures (VMGM) to describe the prior distribution, while we incorporate many nice features of these mixtures to our estimation problem. The corresponding MAP estimation problem is completely expressed in terms of the EM algorithm, which allows for handling nonlinearities and hidden variables that cannot be easily handled with traditional methods. For comparison purposes, we also develop a Coordinate Descent algorithm for the ℓq-norm penalized problem and present the performance results via simulations.

Original languageEnglish (US)
Title of host publication2015 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2015
EditorsDeniz Erdogmus, Serdar Kozat, Jan Larsen, Murat Akcakaya
PublisherIEEE Computer Society
ISBN (Electronic)9781467374545
DOIs
StatePublished - Nov 10 2015
Event25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015 - Boston, United States
Duration: Sep 17 2015Sep 20 2015

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing, MLSP
Volume2015-November
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Conference

Conference25th IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2015
CountryUnited States
CityBoston
Period9/17/159/20/15

Keywords

  • Convergence
  • Maximum likelihood estimation
  • Optimization
  • Parameter estimation
  • Probability density function
  • Signal processing algorithms

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing

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  • Cite this

    Carvajal, R., Aguero, J. C., Godoy, B. I., & Katselis, D. (2015). A map approach for ℓq-norm regularized sparse parameter estimation using the em algorithm. In D. Erdogmus, S. Kozat, J. Larsen, & M. Akcakaya (Eds.), 2015 IEEE International Workshop on Machine Learning for Signal Processing - Proceedings of MLSP 2015 [7324321] (IEEE International Workshop on Machine Learning for Signal Processing, MLSP; Vol. 2015-November). IEEE Computer Society. https://doi.org/10.1109/MLSP.2015.7324321