Abstract
We present a spectral mapping technique for semisupervised pattern classification. Importance scores of features are firstly evaluated with a semi-supervised feature selection algorithm by Zhao et al. Training data are then embedded into a low-dimensional space with a spectral mapping derived from the selected and weighted feature vectors with which test data are classified by the nearest neighbor rule. The performance of the proposed pattern classification algorithm is examined with synthetic and real datasets.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009 |
| Pages | 187-190 |
| Number of pages | 4 |
| Publication status | Published - 2009 |
| Event | 11th IAPR Conference on Machine Vision Applications, MVA 2009 - Yokohama, Japan Duration: May 20 2009 → May 22 2009 |
Other
| Other | 11th IAPR Conference on Machine Vision Applications, MVA 2009 |
|---|---|
| Country | Japan |
| City | Yokohama |
| Period | 5/20/09 → 5/22/09 |
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All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
Cite this
Semi-supervised spectral mapping for enhancing separation between classes. / Du, Weiwei; Urahama, Kiichi.
Proceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009. 2009. p. 187-190.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Semi-supervised spectral mapping for enhancing separation between classes
AU - Du, Weiwei
AU - Urahama, Kiichi
PY - 2009
Y1 - 2009
N2 - We present a spectral mapping technique for semisupervised pattern classification. Importance scores of features are firstly evaluated with a semi-supervised feature selection algorithm by Zhao et al. Training data are then embedded into a low-dimensional space with a spectral mapping derived from the selected and weighted feature vectors with which test data are classified by the nearest neighbor rule. The performance of the proposed pattern classification algorithm is examined with synthetic and real datasets.
AB - We present a spectral mapping technique for semisupervised pattern classification. Importance scores of features are firstly evaluated with a semi-supervised feature selection algorithm by Zhao et al. Training data are then embedded into a low-dimensional space with a spectral mapping derived from the selected and weighted feature vectors with which test data are classified by the nearest neighbor rule. The performance of the proposed pattern classification algorithm is examined with synthetic and real datasets.
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UR - http://www.scopus.com/inward/citedby.url?scp=84872720634&partnerID=8YFLogxK
M3 - Conference contribution
SN - 9784901122092
SP - 187
EP - 190
BT - Proceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009
ER -