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Thunder: a Fast Coordinate Selection Solver for Sparse Learning
Shaogang Ren · Weijie Zhao · Ping Li

Thu Dec 10 09:00 PM -- 11:00 PM (PST) @ Poster Session 6 #1808

L1 regularization has been broadly employed to pursue model sparsity. Despite the non-smoothness, people have developed efficient algorithms by leveraging the sparsity and convexity of the problems. In this paper, we propose a novel active incremental approach to further improve the efficiency of the solvers. We show that our method performs well even when the existing methods fail due to the low sparseness or high solution accuracy request. Theoretical analysis and experimental results on synthetic and real-world data sets validate the advantages of the method.

Author Information

Shaogang Ren (Baidu Research, USA)
Weijie Zhao (Baidu Research)
Ping Li (Baidu Research USA)

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