- SINCO - a greedy coordinate ascent method for sparse inverse covariance selection problem Katya Scheinberg(katyascheinberggmail.com) Irina Rish(irishus.ibm.com) Abstract: In this paper, we consider the sparse inverse covariance selection problem which is equivalent to structure recovery of a Markov Network over Gaussian variables. We introduce a simple but efficient greedy algorithm, called {\em SINCO}, for solving the Sparse INverse COvariance problem. Our approach is based on coordinate ascent method which naturally preserves the sparsity of the inverse covariance matrix. We compare our algorithm to the state-of-art method called {\em glasso} \cite{glasso}, evaluating both computational efficiency and structure-reconstruction accuracy of both methods. We show that the two methods are often comparable in speed and accuracy, however, in some regimes, our method can significantly outperform {\em glasso} in terms of both computational time and structure reconstruction error (particularly, false positive error). Our method has an additional advantage of being easily parallelizable. We also show that the greedy nature of the method is such that one can reproduce the regularization path behavior by applying the method to one instance of the regularization parameter only. Numerical experiments demonstrate advantages of our approach on simulated networks, both random and structured'' (scale-free) ones, where the ground-truth structure is available. We also report promising empirical results on real-life problems with unknown ground-truth structure, such as classification of mental states from fMRI data. Keywords: sparse optimization, inverse covariane selection problem, block-coordinate descent, semidefinite programming Category 1: Convex and Nonsmooth Optimization (Convex Optimization ) Category 2: Linear, Cone and Semidefinite Programming (Semi-definite Programming ) Category 3: Applications -- Science and Engineering (Statistics ) Citation: IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, July 2009 Download: [Postscript][PDF]Entry Submitted: 07/31/2009Entry Accepted: 07/31/2009Entry Last Modified: 07/31/2009Modify/Update this entry Visitors Authors More about us Links Subscribe, Unsubscribe Digest Archive Search, Browse the Repository Submit Update Policies Coordinator's Board Classification Scheme Credits Give us feedback Optimization Journals, Sites, Societies Optimization Online is supported by the Mathematical Programming Society.