Name
Abstract | ||
|---|---|---|
•This is the first attempt to extend Concept Factorization to multi-view setting.•The proposed algorithm learns the view-weight of each view automatically.•A new updating rule is developed to efficiently and effectively seek the solution of the associated optimization problem.•The comparisons indicate the efficiency of the proposed algorithms. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.jvcir.2017.02.019 | Journal of Visual Communication and Image Representation |
Keywords | Field | DocType |
Multi-view learning,Concept factorization,Document clustering,Manifold learning | Convergence (routing),Graph,Data mining,Pattern recognition,Document clustering,Auxiliary function,Factorization,Artificial intelligence,Nonlinear dimensionality reduction,Cluster analysis,Mathematics,Manifold | Journal |
Volume | Issue | ISSN |
48 | C | 1047-3203 |
Citations | PageRank | References |
5 | 0.44 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kun Zhan | 1 | 294 | 17.38 |
| Jinhui Shi | 2 | 32 | 2.88 |
| Jing Wang | 3 | 178 | 10.02 |
| Feng Tian | 4 | 77 | 12.86 |