Name
Abstract | ||
|---|---|---|
Recent advances of subspace clustering have provided a new way of constructing affinity matrices for clustering. Unlike the kernel-based subspace clustering, which needs tedious tuning among infinitely many kernel candidates, the self-expressive models derived from linear subspace assumptions in modern subspace clustering methods are rigorously combined with sparse or low-rank optimization theory ... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/TCYB.2018.2878069 | IEEE Transactions on Cybernetics |
Keywords | Field | DocType |
Probabilistic logic,Optimization,Clustering methods,Clustering algorithms,Sparse matrices,Estimation,Principal component analysis | Kernel (linear algebra),Spectral clustering,Mathematical optimization,Algorithm,Linear subspace,Probabilistic logic,Maximum a posteriori estimation,Cluster analysis,Prior probability,Optimization problem,Mathematics | Journal |
Volume | Issue | ISSN |
50 | 3 | 2168-2267 |
Citations | PageRank | References |
0 | 0.34 | 31 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jieun Lee | 1 | 0 | 0.34 |
| Hyeogjin Lee | 2 | 1 | 1.03 |
| Minsik Lee | 3 | 151 | 15.32 |
| Nojun Kwak | 4 | 862 | 63.79 |