Name
Abstract | ||
|---|---|---|
We extend a graph spectral method for extracting clusters from graphs representing pairwise similarity between data to hypergraph data with hyperedges denoting higher order similarity between data. Our method is robust to noisy outlier data and the number of clusters can be easily determined. The unsupervised method extracts clusters sequentially in the order of the majority of clusters. We derive from the unsupervised algorithm a semi-supervised one which can extract any cluster irrespective of its majority. The performance of those methods is exemplified with synthetic toy data and real image data. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1093/ietisy/e89-d.7.2315 | IEICE Transactions |
Keywords | DocType | Volume |
pairwise similarity,synthetic toy data,outlier data,unsupervised method extracts cluster,unsupervised algorithm,semi-supervised extraction,real image data,higher order similarity,graph spectral method | Journal | E89-D |
Issue | ISSN | Citations |
7 | 1745-1361 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwei Du | 1 | 23 | 7.33 |
| Kohei Inoue | 2 | 100 | 18.92 |
| Kiichi Urahama | 3 | 141 | 32.64 |