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Poster
Exclusive Feature Learning on Arbitrary Structures via $\ell_{1,2}$-norm
Deguang Kong · Ryohei Fujimaki · Ji Liu · Feiping Nie · Chris Ding

Thu Dec 11 11:00 AM -- 03:00 PM (PST) @ Level 2, room 210D
in Poster Session »

Group lasso is widely used to enforce the structural sparsity, which achieves the sparsity at inter-group level. In this paper, we propose a new formulation called ``exclusive group lasso'', which brings out sparsity at intra-group level in the context of feature selection. The proposed exclusive group lasso is applicable on any feature structures, regardless of their overlapping or non-overlapping structures. We give analysis on the properties of exclusive group lasso, and propose an effective iteratively re-weighted algorithm to solve the corresponding optimization problem with rigorous convergence analysis. We show applications of exclusive group lasso for uncorrelated feature selection. Extensive experiments on both synthetic and real-world datasets indicate the good performance of proposed methods.

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Author Information

Deguang Kong (Yahoo Research)
Ryohei Fujimaki (NEC Data Science Research Laboratories)
Ji Liu (Kwai Inc.)
Feiping Nie (University of Texas Arlington)
Chris Ding (University of Texas at Arlington)

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