Learning of Generalized Low-Rank Models: A Greedy Approach
Machine Learning
2016-07-28 v1 Numerical Analysis
Optimization and Control
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.
Cite
@article{arxiv.1607.08012,
title = {Learning of Generalized Low-Rank Models: A Greedy Approach},
author = {Quanming Yao and James T. Kwok},
journal= {arXiv preprint arXiv:1607.08012},
year = {2016}
}