Ming Yuan. Functional nuclear norm and low rank function estimation.
Ming Yuan
2016 Invited talk
in
Workshop: Adaptive and Scalable Nonparametric Methods in Machine Learning
in
Workshop: Adaptive and Scalable Nonparametric Methods in Machine Learning
Abstract
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.
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