Semi-Supervised Classification with Spectral Projection of Multiplicatively Modulated Similarity Data(Pattern Recognition)
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概要
- 論文の詳細を見る
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.
- 社団法人電子情報通信学会の論文
- 2007-09-01
著者
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Urahama Kiichi
Faculty Of Design Kyushu University
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DU Weiwei
Faculty of Design, Kyushu University
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Urahama Kiichi
Faculty Of Computer Science And Systems Engineering Kyushu Institute Of Technology
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Du Weiwei
Faculty Of Design Kyushu University
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