Kernel Partial Least Squares for Stationary Data
Abstract
We consider the kernel partial least squares algorithm for non- parametric regression with stationary dependent data. Probabilistic convergence rates of the kernel partial least squares estimator to the true regression function are established under a source and an effective dimensionality condition. It is shown both theoretically and in simulations that long range dependence results in slower convergence rates. A protein dynamics example shows high predictive power of kernel partial least squares.
Cite
Text
Singer et al. "Kernel Partial Least Squares for Stationary Data." Journal of Machine Learning Research, 2017.Markdown
[Singer et al. "Kernel Partial Least Squares for Stationary Data." Journal of Machine Learning Research, 2017.](https://mlanthology.org/jmlr/2017/singer2017jmlr-kernel/)BibTeX
@article{singer2017jmlr-kernel,
title = {{Kernel Partial Least Squares for Stationary Data}},
author = {Singer, Marco and Krivobokova, Tatyana and Munk, Axel},
journal = {Journal of Machine Learning Research},
year = {2017},
pages = {1-41},
volume = {18},
url = {https://mlanthology.org/jmlr/2017/singer2017jmlr-kernel/}
}