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- W2380147691 abstract "As an important supervised feature extraction method, optimal discriminant plane has great influence in the facial feature reduction. However, traditional optimal discriminant plane is based on Fisher criterion function and it can only be used in supervised pattern. This paper presented a novel method to extend optimal discriminant plane to unsupervised pattern. Using the maximum ratio between fuzzy between-class scatter and within-class scatter in the projection space as its optimization objective, an optimal discriminant vector and fuzzy scatter matrixes in unsupervised pattern could be figured out. With these, obtained a novel feature extraction method based on unsupervised optimal discriminant plane. Compared to principal component analysis(PCA) unsupervised feature extraction algorithm, this method is easier to get the features for the classification. The experimental results for CMU-PIE face database demonstrate that although this method can’t have the same performance of traditional optimal discriminant plane in the condition of unlabelled data, it is superior to principal component analysis when the difference between one class and the another class is big." @default.
- W2380147691 created "2016-06-24" @default.
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- W2380147691 date "2010-01-01" @default.
- W2380147691 modified "2023-09-23" @default.
- W2380147691 title "Face recognition based on unsupervised optimal discriminant plane" @default.
- W2380147691 hasPublicationYear "2010" @default.
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