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A variational method for learning sparse and overcomplete representations

Girolami, M; (2001) A variational method for learning sparse and overcomplete representations. NEURAL COMPUT , 13 (11) 2517 - 2532.

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Abstract

An expectation-maximization algorithm for learning sparse and overcomplete data representations is presented. The proposed algorithm exploits a variational approximation to a range of heavy-tailed distributions whose limit is the Laplacian. A rigorous lower bound on the sparse prior distribution is derived, which enables the analytic marginalization of a lower bound on the data likelihood. This lower bound enables the development of an expectation-maximization algorithm for learning the overcomplete basis vectors and inferring the most probable basis coefficients.

Type: Article
Title: A variational method for learning sparse and overcomplete representations
Keywords: SEPARATION, ALGORITHM
UCL classification: UCL > School of BEAMS > Faculty of Maths and Physical Sciences > Statistical Science
URI: http://discovery.ucl.ac.uk/id/eprint/1339710
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