Name
Abstract | ||
|---|---|---|
Subspace clustering aims to separate the data into clusters under the hypothesis that the samples within the same cluster will lie in the same low-dimensional subspace. Due to the tough pairwise constraints, k-subspace clustering is sensitive to outliers and initialization. In this letter, we present a novel deep architecture for k-subspace clustering to address this issue, called as Deep Weighted... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/LSP.2019.2941368 | IEEE Signal Processing Letters |
Keywords | DocType | Volume |
Feature extraction,Training,Clustering algorithms,Signal processing algorithms,Neural networks,Decoding,Linear programming | Journal | 26 |
Issue | ISSN | Citations |
11 | 1070-9908 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weitian Huang | 1 | 1 | 1.37 |
| Ming Yin | 2 | 202 | 10.61 |
| Jianzhong Li | 3 | 1 | 2.71 |
| Shengli Xie | 4 | 2530 | 161.51 |