Name
Abstract | ||
|---|---|---|
In this paper, we provide a new viewpoint of sequential random processes of the kind F(x), where x is a multivariate vector of covariates, in terms of a smoothing operation governed by a covariance function. By exploiting the eigenvalues and eigenvectors of the covariance function, we represent the smooth function in terms of an orthogonal series over basis functions where the basis function weights depend on the structure of the eigenfunctions with respect to the process F (x). This enables regression using smoothing based on series truncation and low-rank approximation of the covariance matrix. We show that our proposed method compares favorably both to Gaussian process regression, and to Nadaraya-Watson kernel smoothing. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/MLSP.2015.7324380 | 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP) |
Keywords | Field | DocType |
Covariance function,orthonormal basis,eigendecomposition,low rank,de-noising,kernel smoother,regression | Applied mathematics,Artificial intelligence,Matérn covariance function,Covariance,Covariance function,Mathematical optimization,Estimation of covariance matrices,Pattern recognition,Rational quadratic covariance function,Law of total covariance,Smoothing,Covariance matrix,Mathematics | Conference |
ISSN | Citations | PageRank |
1551-2541 | 0 | 0.34 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristina Soguero-Ruiz | 1 | 65 | 12.73 |
| Robert Jenssen | 2 | 370 | 43.06 |