Linear discriminant analysis for data with subcluster structure

Haesun Park, Jaegul Choo, Barry L. Drake, Jinwoo Kang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


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 sat- isfied 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, hierarchi- cal 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.

Original languageEnglish
Title of host publication2008 19th International Conference on Pattern Recognition, ICPR 2008
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)9781424421756
Publication statusPublished - 2008

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Linear discriminant analysis for data with subcluster structure'. Together they form a unique fingerprint.

Cite this