Graphical model selection for Gaussian conditional random fields in the presence of latent variables
We consider the problem of learning a conditional Gaussian graphical model in the presence of latent variables. Building on recent advances in this field, we suggest a method that decomposes the parameters of a conditional Markov random field into the sum of a sparse and a low-rank matrix. We derive...
| Main Authors: | , , |
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| Format: | Journal article |
| Published: |
Taylor and Francis
2018
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