Kernel Measures of Independence for Non-IID Data
2009
Conference Paper
ei
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 |
Department(s): | Empirical Inference |
Bibtex Type: | Conference Paper (inproceedings) |
Event Name: | Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008) |
Event Place: | Vancouver, BC, Canada |
Address: | Red Hook, NY, USA |
Digital: | 0 |
ISBN: | 978-1-605-60949-2 |
Language: | en |
Organization: | Max-Planck-Gesellschaft |
School: | Biologische Kybernetik |
Links: |
PDF
Web |
BibTex @inproceedings{5465, title = {Kernel Measures of Independence for Non-IID Data}, author = {Zhang, X. and Song, L. and Gretton, A. and Smola, A.}, 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}, 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}, doi = {}, month_numeric = {6} } |