Learning Sparse Gaussian Graphical Model with l0-regularization
For the problem of learning sparse Gaussian graphical models, it is desirable to obtain both sparse structures as well as good parameter estimates. Classical techniques, such as optimizing the l1-regularized maximum likelihood or Chow-Liu algorithm, either focus on parameter estimation or constrain...
| Main Authors: | , |
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| Format: | Article |
| Language: | en_US |
| Published: |
2014
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/88969 |