Abstract
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.
Original language | English |
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Pages (from-to) | 469-476 |
Number of pages | 8 |
Journal | Neurocomputing |
Volume | 57 |
Issue number | 1-4 |
DOIs | |
State | Published - Mar 2004 |
Keywords
- EM algorithm
- Maximum a posteriori
- Overcomplete representations