Automatic diagonal loading for Tyler's robust covariance estimator

Teng Zhang, Ami Wiesel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

An approach of regularizing Tyler's robust M-estimator of the co-variance matrix is proposed. We also provide an automatic choice of the regularization parameter in the high-dimensional regime. Simulations show its advantage over the sample covariance estimator and Tyler's M-estimator when data is heavy-tailed and the number of samples is small. Compared with the previous approaches of regularizing Tyler's M-estimator, our approach has a similar performance and a much simpler way of choosing the regularization parameter automatically.

Original languageAmerican English
Title of host publication2016 19th IEEE Statistical Signal Processing Workshop, SSP 2016
PublisherIEEE Computer Society
ISBN (Electronic)9781467378024
DOIs
StatePublished - 24 Aug 2016
Event19th IEEE Statistical Signal Processing Workshop, SSP 2016 - Palma de Mallorca, Spain
Duration: 25 Jun 201629 Jun 2016

Publication series

NameIEEE Workshop on Statistical Signal Processing Proceedings
Volume2016-August

Conference

Conference19th IEEE Statistical Signal Processing Workshop, SSP 2016
Country/TerritorySpain
CityPalma de Mallorca
Period25/06/1629/06/16

Keywords

  • Robust estimation
  • high-dimensional statistics

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Applied Mathematics
  • Signal Processing
  • Computer Science Applications

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