학술논문
Boltzmann Machine and Mean-Field Approximation for Structured Sparse Decompositions
Document Type
Periodical
Author
Source
IEEE Transactions on Signal Processing IEEE Trans. Signal Process. Signal Processing, IEEE Transactions on. 60(7):3425-3438 Jul, 2012
Subject
Language
ISSN
1053-587X
1941-0476
1941-0476
Abstract
Taking advantage of the structures inherent in many sparse decompositions constitutes a promising research axis. In this paper, we address this problem from a Bayesian point of view. We exploit a Boltzmann machine, allowing to take a large variety of structures into account, and focus on the resolution of a marginalized maximum a posteriori problem. To solve this problem, we resort to a mean-field approximation and the “variational Bayes expectation-maximization” algorithm. This approach results in a soft procedure making no hard decision on the support or the values of the sparse representation. We show that this characteristic leads to an improvement of the performance over state-of-the-art algorithms.