Kernel Measures of Conditional Dependence
Abstract
We propose a new measure of conditional dependence of random variables, based on normalized cross-covariance operators on reproducing kernel Hilbert spaces. Unlike previous kernel dependence measures, the proposed criterion does not de- pend on the choice of kernel in the limit of infinite data, for a wide class of ker- nels. At the same time, it has a straightforward empirical estimate with good convergence behaviour. We discuss the theoretical properties of the measure, and demonstrate its application in experiments.
Cite
Text
Fukumizu et al. "Kernel Measures of Conditional Dependence." Neural Information Processing Systems, 2007.Markdown
[Fukumizu et al. "Kernel Measures of Conditional Dependence." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/fukumizu2007neurips-kernel/)BibTeX
@inproceedings{fukumizu2007neurips-kernel,
title = {{Kernel Measures of Conditional Dependence}},
author = {Fukumizu, Kenji and Gretton, Arthur and Sun, Xiaohai and Schölkopf, Bernhard},
booktitle = {Neural Information Processing Systems},
year = {2007},
pages = {489-496},
url = {https://mlanthology.org/neurips/2007/fukumizu2007neurips-kernel/}
}