Semi-Supervised Classification with Spectral Projection of Multiplicatively Modulated Similarity Data

Weiwei DU  Kiichi URAHAMA 

Publication
IEICE TRANSACTIONS on Information and Systems  Vol.E90-D  No.9  pp.1456-1459
Publication Date: 2007/09/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Pattern Recognition
Keyword: 
semi-supervised classification,  inductive learning,  spectral data projection,  multiplicative similarity modulation, 

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Summary: 
A simple and efficient semi-supervised classification method is presented. An unsupervised spectral mapping method is extended to a semi-supervised situation with multiplicative modulation of similarities between data. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data and color image data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis and a previous semi-supervised classification method.