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- W2154227580 abstract "Linear discriminant analysis (LDA) is a widely-used feature extraction method in classification. However, the original LDA has limitations due to the assumption of a unimodal structure for each cluster, which is satisfied in many applications such as facial image data when variations such as angle and illumination can significantly influence the images of the same person. In this paper, we propose a novel method, hierarchical LDA(h-LDA), which takes into account hierarchical subcluster structures in the data sets. Our experiments show that regularized h-LDA produces better accuracy than LDA, PCA, and tensorFaces." @default.
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- W2154227580 date "2008-12-01" @default.
- W2154227580 modified "2023-09-27" @default.
- W2154227580 title "Linear discriminant analysis for data with subcluster structure" @default.
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- W2154227580 doi "https://doi.org/10.1109/icpr.2008.4761084" @default.
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