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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wenming Zheng Li Zhao Cairong Zou |
| Copyright Year | 1990 |
| Abstract | A new nonlinear feature extraction method called kernel Foley-Sammon optimal discriminant vectors (KFSODVs) is presented in this paper. This new method extends the well-known Foley-Sammon optimal discriminant vectors (FSODVs) from linear domain to a nonlinear domain via the kernel trick that has been used in support vector machine (SVM) and other commonly used kernel-based learning algorithms. The proposed method also provides an effective technique to solve the so-called small sample size (SSS) problem which exists in many classification problems such as face recognition. We give the derivation of KFSODV and conduct experiments on both simulated and real data sets to confirm that the KFSODV method is superior to the previous commonly used kernel-based learning algorithms in terms of the performance of discrimination. |
| Sponsorship | IEEE Computational Intelligence Society |
| Page Count | 9 |
| File Size | 770416 |
| Starting Page | 1 |
| Ending Page | 9 |
| File Format | |
| ISSN | 10459227 |
| Volume Number | 16 |
| Issue Number | 1 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Kernel Principal component analysis Linear discriminant analysis Support vector machines Feature extraction Data mining Scattering Support vector machine classification Null space Machine learning null space Face recognition Foley–Sammon optimal discriminant vectors (FSODVs) kernel methods kernel principal component analysis (PCA) |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Computer Networks and Communications Computer Science Applications Software |
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