A novel pattern classification scheme: Classwise Non-Principal Component Analysis (CNPCA)

Guorong Xuan, Peiqi Chai, Xiuming Zhu, Qiuming Yao, Cong Huang, Yun Q. Shi, Dongdong Fu

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

2 Scopus citations

Abstract

This paper1 presents a novel pattern classification scheme: Class-wise Non-Principal Component Analysis (CNPCA), which utilizes the distribution characteristics of the samples in each class. The Euclidean distance in the subspace spanned by the eigenvectors associated with smallest eigenvalues in each class, named CNPCA distance, is adopted as the classification criterion. The number of the smallest eigenvalues is selected in such a way that the classification error in a given database is minimized. It is a constant for the database and can be determined by experiment. The CNPCA classification scheme usually outperforms other classification schemes under the situations of high computational complexity (associated with high dimensionality of features and/or calculation of inverse variance matrix) or high classification error rate (e.g., owing to the scattering of between-class being less than that of within-class). The experiments have demonstrated that this method is promising in practical applications.

Original languageEnglish (US)
Title of host publicationProceedings - 18th International Conference on Pattern Recognition, ICPR 2006
Pages320-323
Number of pages4
DOIs
StatePublished - 2006
Event18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, China
Duration: Aug 20 2006Aug 24 2006

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume3
ISSN (Print)1051-4651

Other

Other18th International Conference on Pattern Recognition, ICPR 2006
CountryChina
CityHong Kong
Period8/20/068/24/06

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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