Inverse Covariance Estimation for High-Dimensional Data in Linear Time and Space: Spectral Methods for Riccati and Sparse Models
We propose maximum likelihood estimation for learning Gaussian graphical models with a Gaussian (ℓ[2 over 2]) prior on the parameters. This is in contrast to the commonly used Laplace (ℓ[subscript 1) prior for encouraging sparseness. We show that our optimization problem leads to a Riccati matrix eq...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Association for Uncertainty in Artificial Intelligence (AUAI)
2014
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| Online Access: | http://hdl.handle.net/1721.1/87050 https://orcid.org/0000-0003-0238-6384 https://orcid.org/0000-0002-2199-0379 |