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• W2043955238 abstract "We address structured covariance estimation in elliptical distributions by assuming that the covariance is a priori known to belong to a given convex set, e.g., the set of Toeplitz or banded matrices. We consider the General Method of Moments (GMM) optimization applied to robust Tyler's scatter M-estimator subject to these convex constraints. Unfortunately, GMM turns out to be non-convex due to the objective. Instead, we propose a new COCA estimator-a convex relaxation which can be efficiently solved. We prove that the relaxation is tight in the unconstrained case for a finite number of samples, and in the constrained case asymptotically. We then illustrate the advantages of COCA in synthetic simulations with structured compound Gaussian distributions. In these examples, COCA outperforms competing methods such as Tyler's estimator and its projection onto the structure set." @default.
• W2043955238 created "2016-06-24" @default.
• W2043955238 creator A5031252824 @default.
• W2043955238 creator A5091732723 @default.
• W2043955238 date "2014-10-01" @default.
• W2043955238 modified "2023-09-28" @default.
• W2043955238 title "Tyler's Covariance Matrix Estimator in Elliptical Models With Convex Structure" @default.
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• W2043955238 doi "https://doi.org/10.1109/tsp.2014.2348951" @default.
• W2043955238 hasPublicationYear "2014" @default.
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