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

Content Provider	IEEE Xplore Digital Library
Author	Guorong Xuan Peiqi Chai Xiuming Zhu Qiuming Yao Cong Huang Shi, Y.Q. Dongdong Fu
Copyright Year	2006
Description	Author affiliation: Dept. of Comput. Sci., Tongji Univ., Shanghai (Guorong Xuan; Peiqi Chai; Xiuming Zhu; Qiuming Yao; Cong Huang)
Abstract	This paper presents a novel pattern classification scheme: classwise 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
Sponsorship	IEEE CPS
Starting Page	320
Ending Page	323
File Size	593728
Page Count	4
File Format	PDF
ISBN	0769525210
ISSN	10514651
DOI	10.1109/ICPR.2006.141
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern classification Pattern analysis Principal component analysis Eigenvalues and eigenfunctions Scattering Space technology Covariance matrix Spatial databases Error analysis Computer science
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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