DocumentCode :
457394
Title :
A Novel Pattern Classification Scheme: Classwise Non-Principal Component Analysis (CNPCA)
Author :
Xuan, Guorong ; Chai, Peiqi ; Zhu, Xiuming ; Yao, Qiuming ; Huang, Cong ; Shi, Yun Q. ; Fu, Dongdong
Author_Institution :
Dept. of Comput. Sci., Tongji Univ., Shanghai
Volume :
3
fYear :
0
fDate :
0-0 0
Firstpage :
320
Lastpage :
323
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
Keywords :
computational complexity; eigenvalues and eigenfunctions; matrix algebra; pattern classification; principal component analysis; CNPCA classification; CNPCA distance; classwise nonprincipal component analysis; eigenvalues; eigenvectors; high classification error rate; high computational complexity; inverse variance matrix; pattern classification; Computer science; Covariance matrix; Eigenvalues and eigenfunctions; Error analysis; Pattern analysis; Pattern classification; Principal component analysis; Scattering; Space technology; Spatial databases;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location :
Hong Kong
ISSN :
1051-4651
Print_ISBN :
0-7695-2521-0
Type :
conf
DOI :
10.1109/ICPR.2006.141
Filename :
1699530
Link To Document :
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