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- W2914014577 abstract "A novel nonlinear dimensionality reduction method named kernel neighborhood preserving discriminant embedding(KNPDE) was proposed in order to extract nonlinear feature in high dimensional face image.The within-class affinity matrix and the between-class similarity matrix were constructed respectively in order to represent the within-class neighborhood geometry and the similarity between the samples from different classes in feature space.KNPDE integrated neighborhood preserving embedding(NPE) with Fisher discriminant criterion by using kernel trick.KNPDE possessed much more power in classification,which preserve the within-class neighborhood geometry in feature space and sufficiently use the between-class discriminant information.Experimental results on the Yale and the UMIST face databases demonstrated the effectiveness of the algorithm." @default.
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- W2914014577 date "2011-10-01" @default.
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- W2914014577 title "Face recognition based kernel neighborhood preserving discriminant embedding" @default.
- W2914014577 doi "https://doi.org/10.3785/j.issn.1008-973x.2011.10.024" @default.
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