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

Authors:
Weiwei Du;Kiichi Urahama
Affiliations:
-;-
Venue:
IEICE - Transactions on Information and Systems
Year:
2007

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Abstract

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.