Name
Abstract | ||
|---|---|---|
The plenty information from multiple views data as well as the complementary information among different views are usually beneficial to various tasks, e.g., clustering, classification, de-noising. Multi-view subspace clustering is based on the fact that the multi-view data are generated from a latent subspace. To recover the underlying subspace structure, the success of the sparse and/or low-rank subspace clustering has been witnessed recently. Despite some state-of-the-art subspace clustering approaches can numerically handle multi-view data, by simultaneously exploring all possible pairwise correlation within views, the high order statistics is often disregarded which can only be captured by simultaneously utilizing all views. As a consequence, the clustering performance for multi-view data is compromised. To address this issue, in this paper, a novel multi-view clustering method is proposed by using textit{t-product} in third-order tensor space. Based on the circular convolution operation, multi-view data can be effectively represented by a textit{t-linear} combination with sparse and low-rank penalty using self-expressiveness. Our extensive experimental results on facial, object, digits image and text data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of many criteria. |
Year | Venue | Field |
|---|---|---|
2016 | arXiv: Computer Vision and Pattern Recognition | Canopy clustering algorithm,Fuzzy clustering,CURE data clustering algorithm,Clustering high-dimensional data,Data stream clustering,Pattern recognition,Correlation clustering,Computer science,Artificial intelligence,Constrained clustering,Cluster analysis,Machine learning |
DocType | Volume | Citations |
Journal | abs/1608.08336 | 1 |
PageRank | References | Authors |
0.35 | 31 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Yin | 1 | 202 | 10.61 |
| Junbin Gao | 2 | 1112 | 119.67 |
| Shengli Xie | 3 | 2530 | 161.51 |
| Yi Guo | 4 | 414 | 44.10 |