SINCO - An Efficient Greedy Method for Learning Sparse INverse COvariance Matrix

author: Katya Scheinberg, Lehigh University
published: Jan. 19, 2010,   recorded: December 2009,   views: 4602


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Herein, we propose a simple greedy algorithm (SINCO) for solving this optimization problem. SINCO solves the primal problem (unlike its predecessors such as COVSEL [10] and glasso [4]), using coordinate ascent, in a greedy manner, thus naturally preserving the sparsity of the solution. As demonstrated by our empirical results, SINCO has better capability in reducing the false-positive error rate (while maintaining similar true positive rate when networks are sufficiently sparse) than glasso [4], because of its greedy incremental nature.

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