Sparse MRF Learning with Priors on Regularization Parameters

In this paper, we consider the sparse inverse covariance selection problem which is equivalent to structure recovery of a Markov Network over Gaussian variables. The problem of regularization parameter(s) selection is addressed in a Bayesian way, by assuming a prior on the parameter(s) and by using MAP optimization to find both the inverse covariance matrix and the unknown parameters. Our general formulation extends prior art by allowing a vector of regularization parameters and is well-suited for learning structured graphs such as scale-free networks where the sparsity of nodes varies significantly. We also introduce a novel and efficient approach to solving the sparse inverse covariance problem that compares favorably to the state-of-art. Our empirical results demonstrate advantages of our approach on structured (scale-free) networks.

By: Katya Scheinberg; Narges Bani Asadi; Irina Rish

Published in: RC24812 in 2009


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