title: A variational method for learning sparse and overcomplete representations.
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.
authors.affiliation: Laboratory of Computing and Information Science, Helsinki University of Technology, Finland.
authors.forename: M
authors.initials: M
authors.lastname: Girolami