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An EM algorithm for learning sparse and overcomplete representations
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DOI：10.1016/j.neucom.2003.12.002.png)
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Abstract
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An expectation-maximization (EM) algorithm for learning sparse and overcomplete representations is presented in this paper. We show that the estimation of the conditional moments of the posterior distribution can be accomplished by maximum a posteriori estimation. The approximate conditional moments,enable the development of an EM algorithm for learning the overcomplete basis vectors and inferring the most probable basis coefficients. (C) 2003 Elsevier B.V. All rights reserved.
Keywords:
overcomplete representations
EM algorithm
maximum a posteriori
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