Semi-Supervised Classification with Spectral Subspace Projection of Data(Pattern Recognition)
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概要
- 論文の詳細を見る
A semi-supervised classification method is presented. A robust unsupervised spectral mapping method is extended to a semi-supervised situation. Our proposed algorithm is derived by linearization of this nonlinear semi-supervised mapping method. Experiments using the proposed method for some public benchmark data reveal that our method outperforms a supervised algorithm using the linear discriminant analysis for the iris and wine data and is also more accurate than a semi-supervised algorithm of the logistic GRF for the ionosphere dataset.
- 社団法人電子情報通信学会の論文
- 2007-01-01
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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