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SINCO - An Efficient Greedy Method for Learning Sparse INverse COvariance Matrix
This video was recorded at NIPS Workshops, Whistler 2009. 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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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
