Empirical Inference
Empirical Inference
Empirical Inference
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 depend on the choice of kernel in the limit of infinite data, for a wide class of kernels. 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.
| Author(s): | Fukumizu, K. and Gretton, A. and Sun, X. and Schölkopf, B. |
| Links: | |
| Book Title: | Advances in neural information processing systems 20 |
| Journal: | Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007 |
| Pages: | 489-496 |
| Year: | 2008 |
| Month: | September |
| Day: | 0 |
| Editors: | JC Platt and D Koller and Y Singer and S Roweis |
| Publisher: | Curran |
| BibTeX Type: | Conference Paper (inproceedings) |
| Address: | Red Hook, NY, USA |
| Event Name: | 21st Annual Conference on Neural Information Processing Systems (NIPS 2007) |
| Event Place: | Vancouver, BC, Canada |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| ISBN: | 978-1-605-60352-0 |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
BibTeX
@inproceedings{4914,
title = {Kernel Measures of Conditional Dependence},
journal = {Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007},
booktitle = {Advances in neural information processing systems 20},
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 depend on the choice of kernel in the limit of infinite data, for a wide class of kernels. 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.},
pages = {489-496},
editors = {JC Platt and D Koller and Y Singer and S Roweis},
publisher = {Curran},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {Red Hook, NY, USA},
month = sep,
year = {2008},
author = {Fukumizu, K. and Gretton, A. and Sun, X. and Sch{\"o}lkopf, B.},
month_numeric = {9}
}