79 search results - page 3 / 16 » Projected Subgradient Methods for Learning Sparse Gaussians |
ICASSP
2009
IEEE
14 years 2 months ago
2009
IEEE
Recently proposed l1-regularized maximum-likelihood optimization methods for learning sparse Markov networks result into convex problems that can be solved optimally and efficien...
PKDD
2010
Springer
13 years 5 months ago
2010
Springer
In this paper, we introduce a simple but efficient greedy algorithm, called SINCO, for the Sparse INverse COvariance selection problem, which is equivalent to learning a sparse Ga...
ESSMAC
2003
Springer
14 years 20 days ago
2003
Springer
Abstract. While there is strong motivation for using Gaussian Processes (GPs) due to their excellent performance in regression and classification problems, their computational com...
TSP
2010
13 years 2 months ago
2010
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
ICML
2009
IEEE
14 years 8 months ago
2009
IEEE
Recent work has shown that one can learn the structure of Gaussian Graphical Models by imposing an L1 penalty on the precision matrix, and then using efficient convex optimization...