Semi-Supervised Classification with Spectral Subspace Projection of Data

Weiwei DU  Kiichi URAHAMA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E90-D   No.1   pp.374-377
Publication Date: 2007/01/01
Online ISSN: 1745-1361
DOI: 
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Pattern Recognition
Keyword: 
semi-supervised classification,  inductive learning,  regularized normalization,  spectral subspace projection,  

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Summary: 
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.