Fu-Sheng Gou

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This paper devises a semi-supervised learning method called semi-supervised linear discriminant clustering (Semi-LDC). The proposed algorithm considers clustering and dimensionality reduction simultaneously by connecting K -means and linear discriminant analysis (LDA). The goal is to find a feature space where the K -means can perform well in the new space.(More)
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