Name
Abstract | ||
|---|---|---|
The autocorrelation matrix of the kernelized input vector is well approximated by the squared Gram matrix (scaled down by the dictionary size). This holds true under the condition that the input covariance matrix in the feature space is approximated by its sample estimate based on the dictionary elements, leading to a couple of fundamental insights into online learning with kernels. First, the eig... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/LSP.2016.2598615 | IEEE Signal Processing Letters |
Keywords | Field | DocType |
Dictionaries,Kernel,Covariance matrices,Measurement,Correlation,Estimation,Eigenvalues and eigenfunctions | Kernel (linear algebra),Mathematical optimization,Pattern recognition,Radial basis function kernel,Kernel embedding of distributions,Autocorrelation matrix,Polynomial kernel,Artificial intelligence,Hyperplane,Covariance matrix,Mathematics,Eigenvalues and eigenvectors | Journal |
Volume | Issue | ISSN |
23 | 10 | 1070-9908 |
Citations | PageRank | References |
2 | 0.36 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masahiro Yukawa | 1 | 272 | 30.44 |
| Klaus-Robert Müller | 2 | 12756 | 1615.17 |