Name
Abstract | ||
|---|---|---|
•To effectively exploit the geometric information embedded in unlabeled datavia the manifold regularization term.•To have a good ability to reduce the negative influence of outliers by exploiting thenon-convex loss function.•To demonstrate the robustness of RSS-ELM in theory from the perspective of reweighted.•To be efficiently solved by the well known CCCP method.•Validity is investigated by comparing it with several related algorithms on multiple image datasets and UCI datasets. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.knosys.2018.06.029 | Knowledge-Based Systems |
Keywords | Field | DocType |
Semi-supervised learning,Extreme learning machine,Robust,Non-convex loss function,CCCP | Convergence (routing),Square (algebra),Linear system,Computer science,Extreme learning machine,Effective method,Outlier,Robustness (computer science),Artificial intelligence,Machine learning,Computational complexity theory | Journal |
Volume | ISSN | Citations |
159 | 0950-7051 | 3 |
PageRank | References | Authors |
0.38 | 25 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huimin Pei | 1 | 10 | 1.82 |
| Kuaini Wang | 2 | 28 | 3.44 |
| Qiang Lin | 3 | 16 | 3.56 |
| Ping Zhong | 4 | 40 | 11.34 |