Name
Abstract | ||
|---|---|---|
•Taking both local and global construction preservation into account.•We have modified common low-rank constraint in our model.•Coupling graph matrix learning and feature space learning by an iteration way.•Coupling subspace learning and feature selection in a unified framework.•The results on eight dataset are competitive in term of classification. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.patrec.2017.08.018 | Pattern Recognition Letters |
Keywords | Field | DocType |
Adaptive structure learning,Sparsity representation,Local structure preservation | Data mining,Feature vector,Dimensionality reduction,Pattern recognition,Feature selection,Computer science,Matrix (mathematics),Feature (computer vision),Structure learning,Minimum redundancy feature selection,Artificial intelligence,Feature learning | Journal |
Volume | ISSN | Citations |
109 | 0167-8655 | 2 |
PageRank | References | Authors |
0.35 | 30 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yonghua Zhu | 1 | 216 | 12.38 |
| Xuejun Zhang | 2 | 70 | 16.55 |
| Rongyao Hu | 3 | 243 | 14.01 |
| Guoqiu Wen | 4 | 46 | 4.62 |