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• W2068753436 abstract "Recently, there has been focus on penalized log-likelihood covariance estimation for sparse inverse covariance (precision) matrices. The penalty is responsible for inducing sparsity, and a very common choice is the convex $l_1$ norm. However, the best estimator performance is not always achieved with this penalty. The most natural sparsity promoting norm is the non-convex $l_0$ penalty but its lack of convexity has deterred its use in sparse maximum likelihood estimation. In this paper we consider non-convex $l_0$ penalized log-likelihood inverse covariance estimation and present a novel cyclic descent algorithm for its optimization. Convergence to a local minimizer is proved, which is highly non-trivial, and we demonstrate via simulations the reduced bias and superior quality of the $l_0$ penalty as compared to the $l_1$ penalty." @default.
• W2068753436 created "2016-06-24" @default.
• W2068753436 creator A5018011442 @default.
• W2068753436 creator A5077692655 @default.
• W2068753436 date "2015-06-01" @default.
• W2068753436 modified "2023-09-26" @default.
• W2068753436 title "<formula formulatype=inline><tex Notation=TeX>$l_{0}$</tex></formula> Sparse Inverse Covariance Estimation" @default.
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• W2068753436 doi "https://doi.org/10.1109/tsp.2015.2416680" @default.
• W2068753436 hasPublicationYear "2015" @default.
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