Home News About Us Contact Contributors Disclaimer Privacy Policy Help FAQ

Home
Search
Quick Search
Advanced
Fulltext
Browse
Collections
Persons
My eDoc
Session History
Login
Name:
Password:
Documentation
Help
Support Wiki
Direct access to
document ID:


          Institute: MPI für biologische Kybernetik     Collection: Biologische Kybernetik     Display Documents



ID: 420011.0, MPI für biologische Kybernetik / Biologische Kybernetik
A Kernel Statistical Test of Independence
Authors:Gretton, A.; Fukumizu, K.; Teo, C.H.; Song, L.; Schölkopf, B.; Smola, A.J.
Editors:Platt, J. C.; Koller, D.; Singer, Y.; Roweis, S.
Date of Publication (YYYY-MM-DD):2008-09
Title of Proceedings:Advances in Neural Information Processing Systems 20: Proceedings of the 2007 Conference
Start Page:585
End Page:592
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description:Whereas kernel measures of independence have been widely applied in machine learning (notably in kernel ICA), there is as yet no method to determine whether they have detected statistically significant dependence. We provide a novel test of the independence hypothesis for one particular kernel independence measure, the Hilbert-Schmidt independence criterion (HSIC). The resulting test costs O(m^2), where m is the sample size. We demonstrate that this test outperforms established contingency table-based tests. Finally, we show the HSIC test also applies to text (and to structured data more generally), for which no other independence test presently exists.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI f�r biologische Kybernetik/Empirical Inference (Dept. Sch�lkopf)
Identifiers:LOCALID:4928
URL:http://books.nips.cc/papers/files/nips20/NIPS2007_...
The scope and number of records on eDoc is subject to the collection policies defined by each institute - see "info" button in the collection browse view.