Tel Aviv University
Applied Mathematics Seminar
Date:       Tuesday January 01, 2013, 15:10
Place:      Schreiber Bldg, Room 309
Speaker: Gilad Lerman, University of Minnesota
Title: Robust Subspace Modeling
Consider a dataset of vector-valued observations that consists of a
modest number of noisy inliers, which are explained well by a
low-dimensional subspace, along with a large number of outliers,
which have no linear structure. We describe a convex optimization
problem that can reliably fit a low-dimensional model to this type of
data. When the inliers are contained in a low-dimensional subspace we
provide a rigorous theory that describes when this optimization can
recover the subspace exactly. We present an efficient algorithm for
solving this optimization problem, whose computational cost is
comparable to that of the non-truncated SVD. We also show that the
sample complexity of the proposed subspace recovery is of the same
order as PCA subspace recovery and we consequently obtain some
nontrivial robustness to noise.
This presentation is based on three joint works: 1) with Teng Zhang,
2) with Michael McCoy, Joel Tropp and Teng Zhang, and 3) with Matthew
Webpage of the applied mathematics seminar:
Dr. Yoel Shkolnisky
Department of Applied Mathematics
School of Mathematical Sciences        phone:  972-3-640-8705
Tel Aviv University,                   fax  :  972-3-640-9357
Tel Aviv, 69978 Israel                 email:   <>
Technion Math Net-2 (TECHMATH2)
Editor: Michael Cwikel   <> 
Announcement from: Yoel Shkolnisky   <>