Name
Abstract | ||
|---|---|---|
We propose randomized techniques for speeding up Kernel Principal Component Analysis on three levels: sampling and quantization of the Gram matrix in training, randomized rounding in evaluating the kernel expansions, and random projections in evaluating the kernel itself. In all three cases, we give sharp bounds on the accuracy of the obtained approximations. Rather intriguingly, all three techniques can be viewed as instantiations of the following idea: replace the kernel function k by a "randomized kernel" which behaves like k in expectation. |
Year | Venue | Keywords |
|---|---|---|
2001 | ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 14, VOLS 1 AND 2 | sampling technique,kernel function,kernel method,randomized rounding,kernel principal component analysis |
Field | DocType | Volume |
Mathematical optimization,Radial basis function kernel,Kernel embedding of distributions,Kernel principal component analysis,Polynomial kernel,Kernel method,Variable kernel density estimation,Kernel regression,Mathematics,Kernel (statistics) | Conference | 14 |
ISSN | Citations | PageRank |
1049-5258 | 62 | 7.98 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dimitris Achlioptas | 1 | 2037 | 174.89 |
| Frank McSherry | 2 | 4289 | 288.94 |
| Bernhard Schölkopf | 3 | 23120 | 3091.82 |