Ming Yuan, Prof.
Date and Time: Jan 22, 2014 ( 1:00 PM)
Location: Orchard room (3280) at the Wisconsin Institute for Discovery Building
The problem of low rank estimation naturally arises in a number of functional or relational data analysis settings, for example when dealing with spatio-temporal data or link prediction with attributes. We consider a unified framework for these problems and devise a novel penalty function to exploit the low rank structure in such contexts. The resulting empirical risk minimization estimator can be shown to be optimal under fairly general conditions.