Projected Subgradient Methods for Learning Sparse Gaussians
Gaussian Markov random fields (GMRFs) are useful in a broad range of applications. In this paper we tackle the problem of learning a sparse GMRF in a high-dimensional space. Our approach uses the ℓ1-norm as a regularization on the inverse covariance mat
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|Source:||Proceedings of the Twenty Fourth Conference on Uncertainty in Artificial Intelligence|
|01_Duchi_Projected_Subgradient_Methods_2008.pdf||183.27 kB||Adobe PDF||Request a copy|
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