Greedy Learning of Generalized Low-Rank Models / 2294
Quanming Yao, James T. Kwok
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.