Kernel Measures of Independence for Non-Iid Data
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 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.
Cite
Text
Zhang et al. "Kernel Measures of Independence for Non-Iid Data." Neural Information Processing Systems, 2008.Markdown
[Zhang et al. "Kernel Measures of Independence for Non-Iid Data." Neural Information Processing Systems, 2008.](https://mlanthology.org/neurips/2008/zhang2008neurips-kernel/)BibTeX
@inproceedings{zhang2008neurips-kernel,
title = {{Kernel Measures of Independence for Non-Iid Data}},
author = {Zhang, Xinhua and Song, Le and Gretton, Arthur and Smola, Alex J.},
booktitle = {Neural Information Processing Systems},
year = {2008},
pages = {1937-1944},
url = {https://mlanthology.org/neurips/2008/zhang2008neurips-kernel/}
}