Name
Title | ||
|---|---|---|
A supervised multi-view feature selection method based on locally sparse regularization and block computing |
Abstract | ||
|---|---|---|
•A supervised multi-view model is proposed to realize a block-based feature selection.•The proposed model is composed of all sharing sub-models in each class.•The sparse regularizer can enhance the sparsity of blocks from features and views.•The proposed algorithm can realize the block separation and independent solution.•Numerical experiments show the effectiveness of our method on large-scale datasets. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.ins.2021.09.009 | Information Sciences |
Keywords | DocType | Volume |
Supervised feature selection,Multi-view learning,Locally sparse regularization,Block computing,ADMM | Journal | 582 |
ISSN | Citations | PageRank |
0020-0255 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qiang Lin | 1 | 16 | 3.56 |
| Min Men | 2 | 2 | 1.37 |
| Liran Yang | 3 | 2 | 3.40 |
| Ping Zhong | 4 | 40 | 11.34 |