Name
Title | ||
|---|---|---|
Affinity and Penalty Jointly Constrained Spectral Clustering With All-Compatibility, Flexibility, and Robustness |
Abstract | ||
|---|---|---|
The existing, semisupervised, spectral clustering approaches have two major drawbacks, i.e., either they cannot cope with multiple categories of supervision or they sometimes exhibit unstable effectiveness. To address these issues, two normalized affinity and penalty jointly constrained spectral clustering frameworks as well as their corresponding algorithms, referred to as type-I affinity and pen... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TNNLS.2015.2511179 | IEEE Transactions on Neural Networks and Learning Systems |
Keywords | Field | DocType |
Clustering algorithms,Robustness,Semisupervised learning,Laplace equations,Image segmentation,Linear programming,Optimization | Pairwise comparison,Spectral clustering,Normalization (statistics),Pattern recognition,Computer science,Image segmentation,Robustness (computer science),Artificial intelligence,Constrained clustering,Linear programming,Cluster analysis,Machine learning | Journal |
Volume | Issue | ISSN |
28 | 5 | 2162-237X |
Citations | PageRank | References |
2 | 0.37 | 19 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pengjiang Qian | 1 | 133 | 11.25 |
| Yizhang Jiang | 2 | 382 | 27.24 |
| S. Wang | 3 | 62 | 3.54 |
| Kuan-Hao Su | 4 | 24 | 5.46 |
| Jun Wang | 5 | 152 | 9.49 |
| lingzhi hu | 6 | 11 | 0.80 |
| Raymond F. Muzic Jr. | 7 | 28 | 4.48 |