A Novel Pattern Classification Scheme: Classwise Non-Principal Component Analysis (CNPCA)

Authors:
Guorong Xuan;Peiqi Chai;Xiuming Zhu;Qiuming Yao;Cong Huang;Yun Q. Shi;Dongdong Fu
Affiliations:
Tongji University, Shanghai, China;Tongji University, Shanghai, China;Tongji University, Shanghai, China;Tongji University, Shanghai, China;Tongji University, Shanghai, China;New Jersey Institute of Technology, USA;New Jersey Institute of Technology, USA
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Year:
2006

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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.