Name
Abstract | ||
|---|---|---|
•A graph learning framework, which captures both the global and local structure in data, is proposed.•Theoretical analysis builds the connections of our model to k-means, spectral clustering, and kernel k-means.•Extensions to semi-supervised classification and multiple kernel learning are presented. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.patcog.2020.107627 | Pattern Recognition |
Keywords | DocType | Volume |
Similarity graph,Rank constraint,Clustering,Semi-supervised classification,Local ang global structure,Kernel method | Journal | 110 |
Issue | ISSN | Citations |
1 | 0031-3203 | 9 |
PageRank | References | Authors |
0.44 | 39 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhao Kang | 1 | 166 | 9.55 |
| Chong Peng | 2 | 288 | 20.54 |
| Qiang Cheng | 3 | 286 | 17.77 |
| Xinwang Liu | 4 | 61 | 8.12 |
| xi peng | 5 | 96 | 6.39 |
| Zenglin Xu | 6 | 923 | 66.28 |
| Ling Tian | 7 | 34 | 8.67 |