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Practical Large-Scale Optimization for Max-Norm Regularization

By Jason Lee, Benjamin Recht, Ruslan Salakhutdinov and Nathan Srebro

Abstract

The max-norm was proposed as a convex matrix regularizer in [1] and was shown to be empirically superior to the trace-norm for collaborative filtering problems. Although the max-norm can be computed in polynomial time, there are currently no practical algorithms for solving large-scale optimization problems that incorporate the max-norm. The present work uses a factorization technique of Burer and Monteiro [2] to devise scalable first-order algorithms for convex programs involving the max-norm. These algorithms are applied to solve huge collaborative filtering, graph cut, and clustering problems. Empirically, the new methods outperform mature techniques from all three areas.

Year: 2011
OAI identifier: oai:CiteSeerX.psu:10.1.1.187.6602
Provided by: CiteSeerX
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