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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(200.7KB)>>
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. | ||||||||||
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