TY - GEN
T1 - Enhanced spectral embedding with semi-supervised feature selection
AU - Du, Weiwei
AU - Urahama, Kiichi
PY - 2009/12/1
Y1 - 2009/12/1
N2 - We present a spectral embedding 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 dataseis.
AB - We present a spectral embedding 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 dataseis.
UR - http://www.scopus.com/inward/record.url?scp=77950553018&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77950553018&partnerID=8YFLogxK
U2 - 10.1109/ICNC.2009.170
DO - 10.1109/ICNC.2009.170
M3 - Conference contribution
AN - SCOPUS:77950553018
SN - 9780769537368
T3 - 5th International Conference on Natural Computation, ICNC 2009
SP - 129
EP - 133
BT - 5th International Conference on Natural Computation, ICNC 2009
T2 - 5th International Conference on Natural Computation, ICNC 2009
Y2 - 14 August 2009 through 16 August 2009
ER -