Name
Abstract | ||
|---|---|---|
A fast algorithm, Accelerated Kernel Feature Analysis (AKFA), that discovers salient features evidenced in a sample of n unclassified patterns, is presented. Like earlier kernel-based feature selection algorithms, AKFA implicitly embeds each pattern into a Hilbert space, H, induced by a Mercer kernel. An ell-dimensional linear subspace of H is iteratively constructed by maximizing a variance condition for the nonlinearly transformed sample. This linear subspace can then be used to define more efficient data representations and pattern classifiers. AKFA requires O(elln2) operations, as compared to 0(n^3) for Sch¨olkof, Smola, and M¨ullers Kernel Principal Component Analysis (KPCA), and O(ell^2 n^2) for Smola, Mangasarian, and Sch¨olkopfs Sparse Kernel Feature Analysis (SKFA). Numerical experiments show that AKFA can generate more concise feature representations than both KPCA and SKFA, and demonstrate that AKFA obtains similar classification performance as KPCA for a face recognition problem. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/CVPR.2006.43 | CVPR (1) |
Keywords | Field | DocType |
sparse kernel feature analysis,n unclassified pattern,accelerated kernel feature analysis,pattern classifier,linear subspace,component analysis,concise feature representation,ell-dimensional linear subspace,kernel principal,kernel-based feature selection algorithm,acceleration,face recognition,principal component analysis,kernel,feature analysis,hilbert space,pattern analysis,kernel principal component analysis,algorithm design and analysis,pattern recognition,feature selection | Pattern recognition,Radial basis function kernel,Computer science,Kernel embedding of distributions,Kernel Fisher discriminant analysis,Kernel principal component analysis,Polynomial kernel,Artificial intelligence,Kernel method,String kernel,Variable kernel density estimation | Conference |
Volume | ISSN | ISBN |
1 | 1063-6919 | 0-7695-2597-0 |
Citations | PageRank | References |
3 | 0.37 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianhua Jiang | 1 | 55 | 6.25 |
| Yuichi Motai | 2 | 230 | 24.68 |
| Robert R. Snapp | 3 | 56 | 52.96 |
| Xingquan Zhu | 4 | 3086 | 181.95 |