Robust Subspace Recovery By Tyler'S M-Estimator

Keywords

M-estimator; Robust statistics; Subspace recovery

Abstract

This paper considers the problem of robust subspace recovery: Given a set of N points in RD, if many lie in a d-dimensional subspace, then can we recover the underlying subspace? We show that Tyler's M-estimator can be used to recover the underlying subspace, if the percentage of the inliers is larger than d/D and the data points lie in general position. Empirically, Tyler's M-estimator compares favorably with other convex subspace recovery algorithms in both simulations and experiments on real data sets.

Publication Date

3-1-2016

Publication Title

Information and Inference

Volume

5

Issue

1

Number of Pages

1-21

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1093/imaiai/iav012

Socpus ID

85051182320 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85051182320

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