A Pathwise Algorithm for Covariance Selection

presenter: Selin Damla Ahipasaoglu, Cornell University
published: Jan. 19, 2010,   recorded: December 2009,   views: 4477


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Covariance selection seeks to estimate a covariance matrix by maximum likelihood while restricting the number of nonzero inverse covariance matrix coefficients. A single penalty parameter usually controls the tradeoff between log likelihood and sparsity in the inverse matrix. We describe an efficient algorithm for computing a full regularization path of solutions to this problem.

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