Name
Abstract | ||
|---|---|---|
Kernel methods are known to be effective for nonlinear multivariate analysis. One of the main issues in the practical use of kernel methods is the selection of kernel. There have been a lot of studies on kernel selection and kernel learning. Multiple kernel learning (MKL) is one of the promising kernel optimization approaches. Kernel methods are applied to various classifiers including Fisher discriminant analysis (FDA). FDA gives the Bayes optimal classification axis if the data distribution of each class in the feature space is a gaussian with a shared covariance structure. Based on this fact, an MKL framework based on the notion of gaussianity is proposed. As a concrete implementation, an empirical characteristic function is adopted to measure gaussianity in the feature space associated with a convex combination of kernel functions, and two MKL algorithms are derived. From experimental results on some data sets, we show that the proposed kernel learning followed by FDA offers strong classification power. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1162/NECO_a_00299 | Neural Computation |
Keywords | Field | DocType |
mkl algorithm,kernel learning,promising kernel optimization approach,mkl framework,multiple kernel learning,proposed kernel,kernel selection,kernel function,kernel method,gaussianity measure,feature space,multivariate analysis,convex combination | Pattern recognition,Radial basis function kernel,Kernel embedding of distributions,Multiple kernel learning,Tree kernel,Kernel principal component analysis,Artificial intelligence,Kernel method,Variable kernel density estimation,Mathematics,Machine learning,Kernel (statistics) | Journal |
Volume | Issue | ISSN |
24 | 7 | 0899-7667 |
Citations | PageRank | References |
5 | 0.47 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hideitsu Hino | 1 | 99 | 25.73 |
| Nima Reyhani | 2 | 191 | 16.25 |
| Noboru Murata | 3 | 855 | 170.36 |