Machine Learning Test conference

Learning Sparse Gaussian Markov Networks using a Greedy Coordinate Ascent Approach

Not scheduled

15m

50 (Bonn)

Machine Learning

Speaker

Mr Katya Scheinberg (Columbia University)

Description

In this paper, we introduce a simple but efficient greedy algorithm,
called SINCO, for the Sparse INverse COvariance selection problem, which is equivalent to learning a sparse Gaussian Markov Network, and empirically investigate the structure-recovery properties of the algorithm. Our approach is based on a coordinate ascent method which naturally preserves the sparsity of the network structure. We show that SINCO is often comparable to, and, in various cases, outperforms commonly used approaches such as glasso [7] and COVSEL [1], in terms of both structure-reconstruction error (particularly, false positive error) and computational time.