Enhanced spectral embedding with semi-supervised feature selection

Authors:
Weiwei Du;Kiichi Urahama
Affiliations:
Department of Information Science, Kyoto Institute of Technology, Kyoto, Japan;Department of Visual Communication Design, Kyushu University, Fukuoka, Japan
Venue:
ICNC'09 Proceedings of the 5th international conference on Natural computation
Year:
2009

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Abstract

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 datasets.