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Content Provider | IEEE Xplore Digital Library |
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Author | Xie Jianhong |
Copyright Year | 2008 |
Description | Author affiliation: Sch. of Electron., Jiangxi Univ. of Finance & Econ., Nanchang (Xie Jianhong) |
Abstract | Kernel principal component analysis (KPCA) is an improved PCA, which possesses the property of extracting optimal features by adopting a nonlinear kernel function method. Based on the duality between least square support vector machine (LS-SVM) and KPCA, the optimization problem of KPCA can be transformed into the solving of quadratic equations by means of LS-SVM method, and thus leads to the computational complexity being simplified largely. Based on ORL face database, KPCA combined with LS-SVM is applied to realize faces recognition. The experimental results show that KPCA based on LS-SVM has a higher correct recognition rate, and a faster computational speed. |
Starting Page | 638 |
Ending Page | 641 |
File Size | 441643 |
Page Count | 4 |
File Format | |
ISBN | 9780769534978 |
DOI | 10.1109/IITA.2008.234 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2008-12-20 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | KPCA Nonlinear equations Face recognition Optimization methods Computational complexity Least squares methods Support vector machines Databases LS-SVM face recognition Feature extraction Kernel Principal component analysis |
Content Type | Text |
Resource Type | Article |
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