Search Machine Learning Repository: @inproceedings{icml2014c2_meng14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Learning Latent Variable Gaussian Graphical Models},
    Url = {http://jmlr.org/proceedings/papers/v32/meng14.pdf},
    Abstract = {Gaussian graphical models (GGM) have been widely used in many high-dimensional applications ranging from biological and financial data to recommender systems. Sparsity in GGM plays a central role both statistically and computationally. Unfortunately, real-world data often does not fit well to sparse graphical models. In this paper, we focus on a family of latent variable Gaussian graphical models (LVGGM), where the model is conditionally sparse given latent variables, but marginally non-sparse. In LVGGM, the inverse covariance matrix has a low-rank plus sparse structure, and can be learned in a regularized maximum likelihood framework. We derive novel parameter estimation error bounds for LVGGM under mild conditions in the high-dimensional setting. These results complement the existing theory on the structural learning, and open up new possibilities of using LVGGM for statistical inference.},
    Author = {Zhaoshi Meng and Brian Eriksson and Al Hero},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {1269-1277}
   }