Name
Abstract | ||
|---|---|---|
Low-rank representation (LRR) has received considerable attention in subspace segmentation due to its effectiveness in exploring low-dimensional subspace structures embedded in data. To preserve the intrinsic geometrical structure of data, a graph regularizer has been introduced into LRR framework for learning the locality and similarity information within data. However, it is often the case that ... |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TIP.2015.2472277 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Manifolds,Data models,Laplace equations,Optimization,Convergence,Noise,Australia | Ambient space,Data modeling,Feature vector,Subspace topology,Pattern recognition,Manifold alignment,Dual graph,Artificial intelligence,Cluster analysis,Manifold,Mathematics | Journal |
Volume | Issue | ISSN |
24 | 12 | 1057-7149 |
Citations | PageRank | References |
37 | 0.85 | 47 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Yin | 1 | 202 | 10.61 |
| Junbin Gao | 2 | 1112 | 119.67 |
| Zhouchen Lin | 3 | 4805 | 203.69 |
| Qinfeng Shi | 4 | 1564 | 74.85 |
| Yi Guo | 5 | 414 | 44.10 |