Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion. In this paper, we extend this criterion to deal with structured and interdependent observations. This is achieved by modeling the structures using undirected graphical models and comparing the Hilbert space embeddings of distributions. We apply this new criterion to independent component analysis and sequence clustering.
| Author(s): | Zhang, X. and Song, L. and Gretton, A. and Smola, A. |
| Book Title: | Advances in neural information processing systems 21 |
| Journal: | Advances in neural information processing systems 21 : 22nd Annual Conference on Neural Information Processing Systems 2008 |
| Pages: | 1937-1944 |
| Year: | 2009 |
| Month: | June |
| Day: | 0 |
| Editors: | Koller, D. , D. Schuurmans, Y. Bengio, L. Bottou |
| Publisher: | Curran |
| Bibtex Type: | Conference Paper (inproceedings) |
| Address: | Red Hook, NY, USA |
| Event Name: | Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008) |
| Event Place: | Vancouver, BC, Canada |
| Digital: | 0 |
| Electronic Archiving: | grant_archive |
| ISBN: | 978-1-605-60949-2 |
| Language: | en |
| Organization: | Max-Planck-Gesellschaft |
| School: | Biologische Kybernetik |
| Links: | |
BibTex
@inproceedings{5465,
title = {Kernel Measures of Independence for Non-IID Data},
journal = {Advances in neural information processing systems 21 : 22nd Annual Conference on Neural Information Processing Systems 2008},
booktitle = {Advances in neural information processing systems 21},
abstract = {Many machine learning algorithms can be formulated in the framework of statistical independence such as the Hilbert Schmidt Independence Criterion. In this paper, we extend this criterion to deal with structured and interdependent observations. This is achieved by modeling the structures using undirected graphical models and comparing the Hilbert space embeddings of distributions. We apply this new criterion to independent component analysis and sequence clustering.},
pages = {1937-1944},
editors = {Koller, D. , D. Schuurmans, Y. Bengio, L. Bottou},
publisher = {Curran},
organization = {Max-Planck-Gesellschaft},
school = {Biologische Kybernetik},
address = {Red Hook, NY, USA},
month = jun,
year = {2009},
slug = {5465},
author = {Zhang, X. and Song, L. and Gretton, A. and Smola, A.},
month_numeric = {6}
}