Name
Abstract | ||
|---|---|---|
Kernel method is a powerful technique in machine learning and it has been widely applied to feature extraction and classification. Symmetrical principal component analysis (SPCA) is an excellent feature extraction method for face classification because it utilizes the symmetry of the facial images. This paper presents one Kernel based SPCA (KSPCA) algorithm which gives the closed form for polynomial kernel. KSPCA combines advantages of SPCA with kernel method, i.e., KSPCA not only makes use of the symmetry of the facial images, but also extracts nonlinear principal components which contain more abundant information. We compare the performance of SPCA, kernel PCA (KPCA) with KSPCA on CBCL database for binary classification, and on ORL and Yale face database for multi-category classification, respectively. The experimental results show that KSPCA outperforms both SPCA and KPCA. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.neucom.2006.10.019 | Neurocomputing |
Keywords | Field | DocType |
symmetrical principal component analysis,face classification,facial image,odd–even decomposition principle,kernel method,feature extraction,polynomial kernel,excellent feature extraction method,cbcl database,kernel principal component analysis,kernel pca,kernel based symmetrical principal component analysis,binary classification,multi-category classification,principal component,principal component analysis,feature space,machine learning,reproducing kernel hilbert space,eigenvectors | Pattern recognition,Binary classification,Radial basis function kernel,Principal component regression,Kernel embedding of distributions,Kernel principal component analysis,Feature extraction,Polynomial kernel,Artificial intelligence,Kernel method,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
70 | 4-6 | Neurocomputing |
Citations | PageRank | References |
5 | 0.51 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Congde Lu | 1 | 14 | 2.72 |
| Chunmei Zhang | 2 | 5 | 0.85 |
| Taiyi Zhang | 3 | 176 | 17.60 |
| Wei Zhang | 4 | 226 | 19.22 |