University of Michigan
EECS Department
Electrical and
Computer Engineering
EECS Building
1301 Beal Avenue
Ann Arbor, MI 481092122
CSP Seminar
Convergence of a randomized sampling method for identifying a subspace of R^n.
Laura Balzano
Assistant Professor
University of Michigan, Department of EECS 

Thursday, February 21, 2013
4:00pm  5:00pm 1005 EECS


About the EventResearch on incremental gradient algorithms and gradient algorithms on manifolds are both gaining popularity in the last decade; incremental gradient because of its speed, adaptivity to realtime data, and low storage requirements, and manifold modeling in general because of its generality while still maintaining a belief that highdimensional data may exhibit some structure that we can leverage for prediction and estimation. In this talk I'll briefly review some of the convergence results in those areas that we all should know. Then I will discuss convergence of the GROUSE algorithm for identifying subspaces. GROUSE takes as input a sequence of vectors which, as a collection, lie in a fixed subspace. Each vector is observed only on a subset of the vector's coordinates. The algorithm generates a sequence of orthonormal matrices whose columns span the latest estimate of the subspace. We present a local convergence analysis for the case in which both the vector and the subset of observed coordinates are selected randomly and independently at each iteration, showing that the method exhibits an almost linear convergence rate. Additionally, we describe the relationship between GROUSE and methods of incremental SVD type.

Additional Information
Contact: Ann Pace
Phone: 7635022
Email: ampace@umich.edu
Sponsor: University of Michigan
Open to: Public
Presentation: http://www.eecs.umich.edu/systems/cspseminars/balzano_2_21_13.pdf


