Robust recovery of multiple subspaces by geometric l_p minimization

Details Robust recovery of multiple subspaces by geometric l_p minimization Robust recovery of multiple subspaces by geometric l_p minimization

2011-04-19

arxiv.org/abs/1104.3770v2

Annals of Statistics 2011, Vol. 39, No. 5, 2686-2715

Statistics

Machine Learning

Published in at http://dx.doi.org/10.1214/11-AOS914 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of

Scientific paper

10.1214/11-AOS914

We assume i.i.d. data sampled from a mixture distribution with K components along fixed d-dimensional linear subspaces and an additional outlier component. For p>0, we study the simultaneous recovery of the K fixed subspaces by minimizing the l_p-averaged distances of the sampled data points from any K subspaces. Under some conditions, we show that if $01 and p>1, then the underlying subspaces cannot be recovered or even nearly recovered by l_p minimization. The results of this paper partially explain the successes and failures of the basic approach of l_p energy minimization for modeling data by multiple subspaces.

Affiliated with

Also associated with

No associations

Rating

Robust recovery of multiple subspaces by geometric l_p minimization does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Robust recovery of multiple subspaces by geometric l_p minimization, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Robust recovery of multiple subspaces by geometric l_p minimization will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-183380