Name
Abstract | ||
|---|---|---|
Spectral clustering (SC) is one of the most widely used clustering methods. In this letter, we extend the traditional SC with a semi-supervised manner. Specifically, with the guidance of small amount of supervisory information, we build a matrix with anti-block-diagonal appearance, which is further utilized to regularize the product of the low-dimensional embedding and its transpose. Technically, ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/LSP.2018.2791606 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Optimization,Clustering algorithms,Clustering methods,Signal processing algorithms,Convergence,Mutual information,Eigenvalues and eigenfunctions | Convergence (routing),Spectral clustering,Mathematical optimization,Transpose,Augmented Lagrangian method,Regularization (mathematics),Mutual information,Cluster analysis,Convex optimization,Mathematics | Journal |
Volume | Issue | ISSN |
25 | 3 | 1070-9908 |
Citations | PageRank | References |
3 | 0.37 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuheng Jia | 1 | 93 | 13.13 |
| Sam Kwong | 2 | 4590 | 315.78 |
| Junhui Hou | 3 | 395 | 49.84 |