Name
Abstract | ||
|---|---|---|
The covariance descriptor which is a symmetric positive definite (SPD) matrix, has recently attracted considerable attentions in computer vision. However, it is not trivial issue to handle its non-linearity in semi-supervised learning. To this end, in this paper, a semi-supervised sparse subspace clustering on SPD manifolds is proposed, via considering the intrinsic geometric structure within the manifold-valued data. Experimental results on two databases show that our method can provide better clustering solutions than the state-of-the-art approaches thanks to incorporating Riemannian geometry structure. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-981-10-3002-4_49 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Subspace clustering,Sparse subspace clustering,Graph,Semi-supervised,SPD matrix | Subspace clustering,Algebra,Matrix (mathematics),Computer science,Positive-definite matrix,Symmetric rank-one,Cluster analysis,Riemannian geometry,Manifold,Covariance | Conference |
Volume | ISSN | Citations |
662 | 1865-0929 | 1 |
PageRank | References | Authors |
0.36 | 22 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Yin | 1 | 202 | 10.61 |
| Xiaozhao Fang | 2 | 1 | 0.36 |
| Shengli Xie | 3 | 2530 | 161.51 |