Name
Abstract | ||
|---|---|---|
•We propose a new feature selection method based on graph-based feature representations and the fused lasso framework.•Our approach can accommodate structural relationship between pairs of samples through graph-based features.•Our method can enhance the trade-off between the relevance of each feature and the redundancy between pairwise features.•An iterative algorithm is developed to identify the most discriminative features.•Experiments demonstrate that our proposed approach can outperform its competitors on benchmark datasets. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2021.108058 | Pattern Recognition |
Keywords | DocType | Volume |
Feature selection,Structural relationship,Fused lasso,Graph-based feature selection,Sparse learning,Correlated feature group | Journal | 119 |
Issue | ISSN | Citations |
1 | 0031-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lixin Cui | 1 | 3 | 2.74 |
| Lu Bai | 2 | 22 | 3.11 |
| Yue Wang | 3 | 2 | 1.45 |
| Philip S. Yu | 4 | 30670 | 3474.16 |
| Edwin R. Hancock | 5 | 5432 | 462.92 |