Name
Title | ||
|---|---|---|
High-dimensional Gaussian graphical model selection: walk summability and local separation criterion |
Abstract | ||
|---|---|---|
We consider the problem of high-dimensional Gaussian graphical model selection. We identify a set of graphs for which an efficient estimation algorithm exists, and this algorithm is based on thresholding of empirical conditional covariances. Under a set of transparent conditions, we establish structural consistency (or sparsistency) for the proposed algorithm, when the number of samples n = Ω(Jmin-2 log p), where p is the number of variables and Jmin is the minimum (absolute) edge potential of the graphical model. The sufficient conditions for sparsistency are based on the notion of walk-summability of the model and the presence of sparse local vertex separators in the underlying graph. We also derive novel non-asymptotic necessary conditions on the number of samples required for sparsistency. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.5555/2503308.2503317 | Journal of Machine Learning Research |
Keywords | Field | DocType |
derive novel non-asymptotic,edge potential,local separation criterion,jmin-2 log p,necessary condition,proposed algorithm,samples n,model selection,graphical model,efficient estimation algorithm,high-dimensional gaussian graphical model,empirical conditional covariances,engineering | Graph,Vertex (geometry),Gaussian,Artificial intelligence,Thresholding,Graphical model,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
13 | 1 | 1532-4435 |
Citations | PageRank | References |
21 | 1.00 | 35 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Animashree Anandkumar | 1 | 1629 | 116.30 |
| Vincent Yan Fu Tan | 2 | 490 | 76.15 |
| Huang, Furong | 3 | 107 | 14.58 |
| Alan S. Willsky | 4 | 7466 | 847.01 |