Greedy Learning of Generalized Low-Rank Models
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 2294-2299.
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.
@inproceedings{ijcai2016p327,
title = {Greedy Learning of Generalized Low-Rank Models},
author = {Yao, Quanming and Kwok, James T.},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {2294--2299},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/327},
}
TY - CPAPER TI - Greedy Learning of Generalized Low-Rank Models AU - Yao, Quanming AU - Kwok, James T. T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 2294 EP - 2299 UR - https://ijcai.org/proceedings/2016/327 ER -Copied
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