101 search results - page 2 / 21 » L0 sparse graphical modeling |
ICASSP
2011
IEEE
12 years 8 months ago
2011
IEEE
Graphical models are useful for capturing interdependencies of statistical variables in various fields. Estimating parameters describing sparse graphical models of stationary mul...
ICML
2009
IEEE
14 years 5 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...
ICML
2006
IEEE
14 years 5 months ago
2006
IEEE
We consider the problem of fitting a large-scale covariance matrix to multivariate Gaussian data in such a way that the inverse is sparse, thus providing model selection. Beginnin...
JMLR
2010
12 years 11 months ago
2010
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
JMLR
2010
12 years 11 months ago
2010
