  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.
