We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion.
- Authors
Anandkumar, Animashree; Tan, Vincent Y. F.; Huang, Furong; Willsky, Alan S.; Wainwright, Martin
- 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 Due to image rights restrictions, multiple line equation(s) cannot be graphically displayed. 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.
- Subjects
GAUSSIAN processes; GRAPHICAL modeling (Statistics); COMPUTER algorithms; MATHEMATICAL models; GRAPH theory; ANALYSIS of covariance; EMPIRICAL research
- Publication
Journal of Machine Learning Research, 2012, Vol 13, Issue 8, p2293
- ISSN
1532-4435
- Publication type
Article