Linear Discriminant Analysis for Data with Subcluster Structure

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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.
Publisher
IEEE Computer Society
Issue Date
2008-12
Language
English
Citation

19th International Conference on Pattern Recognition (ICPR 2008), pp.579 - +

ISSN
1051-4651
DOI
10.1109/ICPR.2008.4761084
URI
http://hdl.handle.net/10203/273505
Appears in Collection
RIMS Conference Papers
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