Name
Abstract | ||
|---|---|---|
Subspace clustering aims to partition the data points drawn from a union of subspaces according to their underlying subspaces. For accurate semi-supervised subspace clustering, all data that have a must-link constraint or the same label should be grouped into the same underlying subspace. However, this is not guaranteed in existing approaches. Moreover, these approaches require additional paramete... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TCYB.2016.2618852 | IEEE Transactions on Cybernetics |
Keywords | Field | DocType |
Sparse matrices,Clustering methods,Robustness,Noise measurement,Linear programming,Cybernetics | Fuzzy clustering,CURE data clustering algorithm,Artificial intelligence,Cluster analysis,Sparse matrix,Mathematical optimization,Subspace topology,Pattern recognition,Correlation clustering,Linear subspace,Constrained clustering,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
47 | 12 | 2168-2267 |
Citations | PageRank | References |
7 | 0.40 | 42 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing Wang | 1 | 178 | 10.02 |
| Xiao Wang | 2 | 445 | 29.80 |
| Feng Tian | 3 | 77 | 12.86 |
| Chang Hong Liu | 4 | 36 | 3.26 |
| Hongchuan Yu | 5 | 116 | 12.72 |