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
DOI: 10.1093/ietisy/e90-d.9.1456
Print ISSN: 0916-8532
Type of Manuscript: LETTER
Category: Pattern Recognition
Keyword: 
semi-supervised classification,  inductive learning,  spectral data projection,  multiplicative similarity modulation,  

Full Text: PDF>>
Buy this Article




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.