Name
Abstract | ||
|---|---|---|
Current multi-view clustering algorithms use multistage strategies to conduct clustering, or require cluster number or similarity matrix prior, or suffer influence of irrelevant features and outliers. In this paper, we propose a Joint Robust Multi-view (JRM) spectral clustering algorithm that considers information from all views of the multi-view dataset to conduct clustering and solves the issues, such as initialization, cluster number determination, similarity measure, feature selection, and outlier reduction around clustering, in a unified way. The optimal performance could be reached when all views are considered and the separated stages are combined in a unified way. Experiments have been performed on six real-world benchmark datasets and our proposed JRM algorithm outperforms the comparison clustering algorithms in terms of two evaluation metrics for clustering algorithms including accuracy and purity. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11063-020-10257-0 | NEURAL PROCESSING LETTERS |
Keywords | DocType | Volume |
Clustering, Multi-view, k-means clustering, Spectral clustering, Feature selection, Outlier reduction, Similarity measure | Journal | 52 |
Issue | ISSN | Citations |
3 | 1370-4621 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tong Liu | 1 | 47 | 12.77 |
| Gaven Martin | 2 | 0 | 0.68 |
| YongXin Zhu | 3 | 0 | 0.34 |
| Lin Peng | 4 | 0 | 0.34 |
| Li Li | 5 | 235 | 35.19 |