- Series
- Other Talks
- Time
- Monday, February 13, 2012 - 11:00am for 1 hour (actually 50 minutes)
- Location
- Skiles 170
- Speaker
- Pedro Rangel – School of Mathematics, Georgia Tech
- Organizer
- Pedro Rangel Walteros

The goal in matrix recovery problems is to estimate an unknown rank-r
matrix S of size m based on a set of n observations. It is easy to see
that even in the case where the observations are not contaminated with
noise, there exist low rank matrices that cannot be recovered based on n
observations unless n is very large. In order to deal with these cases,
Candes and Tao introduced the called low-coherence assumptions and a
parameter \nu measuring how low-coherent the objective matrix S is.
Using the low-coherence assumptions, Gross proved that S can be recovered
with high probability if n>O(\nu r m \log^2(m)) by an estimator based on
nuclear norm penalization. Let's consider the generalization of the matrix
recovery problem where the matrix S is not only low-rank but also "smooth"
with respect to the geometry given by a graph G. In this 40 minutes long
talk, the speaker will present an approximation error bound for a proposed
estimator in this generalization of the matrix recovery problem.