Name
Abstract | ||
|---|---|---|
GoDec+ shows its robustness in low-rank matrix decomposition but only deals with single-view data. This paper extends GoDec+ to multi-view data by jointly learning latent space and multi-view fusion feature. The proposed method factorizes the low-rank matrix in GoDec+ into the product of a basis matrix of the latent space and a shared representation given by a transformation matrix. By constraining the basis matrix to be group sparse, the proposed method treats the effects of different views differently. Extensive experiments show that the proposed method learns a good fusion feature and outperforms the compared methods in image classification and annotation. |
Year | Venue | Field |
|---|---|---|
2017 | ICIMCS | Annotation,Pattern recognition,Matrix (mathematics),Computer science,Matrix decomposition,Fusion,Robustness (computer science),Artificial intelligence,Transformation matrix,Contextual image classification,Feature learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
10 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kailing Guo | 1 | 12 | 4.56 |
| Xiangmin Xu | 2 | 343 | 22.97 |
| Bolun Cai | 3 | 270 | 16.48 |
| Tong Zhang | 4 | 53 | 18.56 |