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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

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.

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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