Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2014 | NIPS | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Deguang Kong | 1 | 374 | 21.68 |
| Ryohei Fujimaki | 2 | 193 | 16.93 |
| Liu, Ji | 3 | 0 | 0.34 |
| Feiping Nie | 4 | 7061 | 309.42 |
| Chris Ding | 5 | 9308 | 501.21 |