Learning of Generalized Low-Rank Models: A Greedy Approach

Quanming Yao; James T. Kwok

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arXiv · 1607.08012

Learning of Generalized Low-Rank Models: A Greedy Approach

Quanming Yao·James T. Kwok

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Abstract

Learning of low-rank matrices is fundamental to many machine learning applications. A state-of-the-art algorithm is the rank-one matrix pursuit (R1MP). However, it can only be used in matrix completion problems with the square loss. In this paper, we develop a more flexible greedy algorithm for generalized low-rank models whose optimization objective can be smooth or nonsmooth, general convex or strongly convex. The proposed algorithm has low per-iteration time complexity and fast convergence rate. Experimental results show that it is much faster than the state-of-the-art, with comparable or even better prediction performance.

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Quanming Yao, James T. Kwok. 2016-07-27. Learning of Generalized Low-Rank Models: A Greedy Approach. https://arxiv.org/abs/1607.08012

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