Simple Atom Selection Strategy for Greedy Matrix Completion
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 1799-1804.
Abstract
In this paper we focus on the greedy matrix completion problem. A simple atom selection strategy is proposed building upon an alternating minimization procedure. Based on this per-iteration strategy, we devise a greedy algorithm OAMC and establish an upper bound of the approximation error. To evaluate different weight refinement methods, several variants of OAMC are designed. We prove that OAMC and three of its variants have the property of linear convergence. Experiments of Recommendation and Image Recovery are conducted to make empirical evaluation. Results are promising. We report that our algorithm takes only 700 seconds to process Yahoo Music dataset in PC, yet achieves a root mean square error 24.5 on test set.
@inproceedings{ijcai2015p256,
title = {Simple Atom Selection Strategy for Greedy Matrix Completion},
author = {Shen, Zebang and Qian, Hui and Zhou, Tengfei and Wang, Song},
booktitle = {Proceedings of the Twenty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-15}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Qiang Yang and Michael Wooldridge},
pages = {1799--1804},
year = {2015},
month = {7},
note = {Main Track},
url = {https://ijcai.org/proceedings/2015/256},
}
TY - CPAPER TI - Simple Atom Selection Strategy for Greedy Matrix Completion AU - Shen, Zebang AU - Qian, Hui AU - Zhou, Tengfei AU - Wang, Song T2 - Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15 PB - International Joint Conferences on Artificial Intelligence PY - 2015 SP - 1799 EP - 1804 UR - https://ijcai.org/proceedings/2015/256 ER -Copied
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