Name
Abstract | ||
|---|---|---|
Subspace clustering is a powerful generalization of clustering for high-dimensional data analysis, where low-rank cluster structure is leveraged for accurate inference. K-Subspaces (KSS), an alternating algorithm that mirrors K-means, is a classical approach for clustering with this model. Like K-means, KSS is highly sensitive to initialization, yet KSS has two major handicaps beyond this issue. F... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/JSTSP.2018.2869363 | IEEE Journal of Selected Topics in Signal Processing |
Keywords | Field | DocType |
Clustering algorithms,Signal processing algorithms,Principal component analysis,Robustness,Algorithm design and analysis | Mathematical optimization,Subspace topology,Computer science,Outlier,Algorithm,Robustness (computer science),Linear subspace,Synthetic data,Initialization,Cluster analysis,Computational complexity theory | Journal |
Volume | Issue | ISSN |
12 | 6 | 1932-4553 |
Citations | PageRank | References |
3 | 0.39 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andrew Gitlin | 1 | 3 | 0.39 |
| Biaoshuai Tao | 2 | 26 | 4.69 |
| Laura Balzano | 3 | 410 | 27.51 |
| John Lipor | 4 | 18 | 3.53 |