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IEICE Transactions on Information and Systems 2007 E90-D(1):374-377; doi:10.1093/ietisy/e90-1.1.374
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Copyright © 2007 The Institute of Electronics, Information and Communication Engineers

Regular Section -- Letters -- Pattern Recognition

Semi-Supervised Classification with Spectral Subspace Projection of Data

Weiwei DU1 and Kiichi URAHAMA1

1 The authors are with the Faculty of Design, Kyushu University, Fukuoka-shi, 815–8540 Japan. E-mail: urahama{at}design.kyushu-u.ac.jp


  Abstract

A semi-supervised classification method is presented. A robust unsupervised spectral mapping method is extended to a semi-supervised situation. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis for the iris and wine data and is also more accurate than a semi-supervised algorithm of the logistic GRF for the ionosphere dataset.

Key Words: semi-supervised classification, inductive learning, regularized normalization, spectral subspace projection

Manuscript received July 18, 2006. Manuscript revised September 5, 2006.


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