Covariance 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. Structured Robust Covariance Estimation 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.
Publisher: now publishers Inc
Number of pages: 104
Weight: 163 g
Dimensions: 234 x 156 x 6 mm
You may also be interested in...
£19.25Mixed media product
£52.99Mixed media product
Please sign in to write a review
Thank you for your reservation
Your order is now being processed and we have sent a confirmation email to you at
When will my order be ready to collect?
Call us on or send us an email at
Unfortunately there has been a problem with your order
Please try again or alternatively you can contact your chosen shop on or send us an email at