TY - GEN
T1 - Semi-supervised spectral mapping for enhancing separation between classes
AU - Du, Weiwei
AU - Urahama, Kiichi
PY - 2009/12/1
Y1 - 2009/12/1
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.
UR - http://www.scopus.com/inward/record.url?scp=84872720634&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872720634&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84872720634
SN - 9784901122092
T3 - Proceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009
SP - 187
EP - 190
BT - Proceedings of the 11th IAPR Conference on Machine Vision Applications, MVA 2009
T2 - 11th IAPR Conference on Machine Vision Applications, MVA 2009
Y2 - 20 May 2009 through 22 May 2009
ER -