Name
Abstract | ||
|---|---|---|
A semi-supervised classification method is presented. A robust unsupervised spectral mapping method is extended to a semi-supervised situation. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis for the iris and wine data and is also more accurate than a semi-supervised algorithm of the logistic GRF for the ionosphere dataset. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1093/ietisy/e90-1.1.374 | IEICE Transactions |
Keywords | Field | DocType |
nonlinear semi-supervised mapping method,spectral subspace projection,robust unsupervised spectral mapping,public benchmark data,supervised algorithm,wine data,semi-supervised algorithm,semi-supervised situation,proposed algorithm,semi-supervised classification,semi-supervised classification method | Nonlinear system,Subspace topology,Pattern recognition,Computer science,Spectral mapping,Software,Artificial intelligence,Linear discriminant analysis,Linearization | Journal |
Volume | Issue | ISSN |
E90-D | 1 | 1745-1361 |
Citations | PageRank | References |
1 | 0.41 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwei Du | 1 | 23 | 7.33 |
| Kiichi Urahama | 2 | 141 | 32.64 |