Online Passive-Aggressive Algorithms for Non-Negative Matrix Factorization and Completion

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Mathieu Blondel, Yotaro Kubo, Ueda Naonori ;
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:96-104, 2014.

Abstract

Stochastic Gradient Descent (SGD) is a popular online algorithm for large-scale matrix factorization. However, SGD can often be difficult to use for practitioners, because its performance is very sensitive to the choice of the learning rate parameter. In this paper, we present non-negative passive-aggressive (NN-PA), a family of online algorithms for non-negative matrix factorization (NMF). Our algorithms are scalable, easy to implement and do not require the tedious tuning of a learning rate parameter. We demonstrate the effectiveness of our algorithms on three large-scale matrix completion problems and analyze them in the regret bound model.

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