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• W2574424311 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." @default.
• W2574424311 created "2017-01-26" @default.
• W2574424311 creator A5072484211 @default.
• W2574424311 creator A5073304137 @default.
• W2574424311 date "2016-07-09" @default.
• W2574424311 modified "2023-09-26" @default.
• W2574424311 title "Greedy learning of generalized low-rank models" @default.
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• W2574424311 hasPublicationYear "2016" @default.
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