| Presentation | 2008-12-18 Laplacian Eigenmaps with Semi-Supervised Feature Selection for Pattern Classification Weiwei DU, Kiichi URAHAMA, |
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| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Similarity in the Laplacian eigenmap is the Gaussian function of Euclidean distance between feature vectors. In this paper, we extend it to an anisotropic Laplacian eigenmap with a weighted Euclidean distance. Its weights are learned with the semi-supervised feature selection method by Zhao et al. on the basis of partially labeled training data. All the training data are mapped into a low-dimensional space with the Laplacian eigenmap with learned weights and every unlabeled datum is labeled with the nearest neighbor rule. A test datum is classified also with the nearest neighbor rule with the weighted Euclidean distance. Performance of the proposed method is examined with a synthetic dataset and some real data. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | semi-supervised learning / Laplacian eigenmap / multivaritae mapping / semi-supervised feature selection |
| Paper # | PRMU2008-152 |
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| Conference Information | |
| Committee | PRMU |
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| Conference Date | 2008/12/11(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Registration To | Pattern Recognition and Media Understanding (PRMU) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Laplacian Eigenmaps with Semi-Supervised Feature Selection for Pattern Classification |
| Sub Title (in English) | |
| Keyword(1) | semi-supervised learning |
| Keyword(2) | Laplacian eigenmap |
| Keyword(3) | multivaritae mapping |
| Keyword(4) | semi-supervised feature selection |
| 1st Author's Name | Weiwei DU |
| 1st Author's Affiliation | Kyoto Institute of Technology() |
| 2nd Author's Name | Kiichi URAHAMA |
| 2nd Author's Affiliation | Kyushu University |
| Date | 2008-12-18 |
| Paper # | PRMU2008-152 |
| Volume (vol) | vol.108 |
| Number (no) | 363 |
| Page | pp.pp.- |
| #Pages | 6 |
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