Robust subspace recovery by Tyler's M-estimator

Abstract

This paper considers the problem of robust subspace recovery: given a set of N points in R^D, 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.

Tasks

Reproductions