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OverviewCovariance matrices have found applications in many diverse areas. These include beamforming in array processing; portfolio analysis in finance; classification of data and the handling of high-frequency data. Structured Robust Covariance Estimation considers the estimation of covariance matrices in non-standard conditions including heavy-tailed distributions and outlier contamination. Prior knowledge on the structure of these matrices is exploited in order to improve the estimation accuracy. The distributions, structures and algorithms are all based on an extension of convex optimization to manifolds. It also provides a self-contained introduction and survey of the theory known as geodesic convexity. This is a generalized form of convexity associated with positive definite matrix variables. The fundamental g-convex sets and functions are detailed, along with the operations that preserve them, and their application to covariance estimation. This monograph will be of interest to researchers and students working in signal processing, statistics and optimization. Full Product DetailsAuthor: Ami Wiesel , Teng ZhangPublisher: now publishers Inc Imprint: now publishers Inc Dimensions: Width: 15.60cm , Height: 0.60cm , Length: 23.40cm Weight: 0.165kg ISBN: 9781680830941ISBN 10: 1680830945 Pages: 108 Publication Date: 22 December 2015 Audience: College/higher education , Postgraduate, Research & Scholarly Format: Paperback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of Contents1: Preliminaries 2: Robust covariance estimation 3: Tyler’s estimator 4: Regularization 5: G-convex structure 6: Extensions ReferencesReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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