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



ID: 461834.0, MPI für biologische Kybernetik / Biologische Kybernetik
Kernel Measures of Independence for Non-IID Data
Authors:Zhang, X.; Song, L.; Gretton, A.; Smola, A.
Editors:Koller, D.; Schuurmans, D.; Bengio, Y.; Bottou, L.
Date of Publication (YYYY-MM-DD):2009-06
Title of Proceedings:Advances in Neural Information Processing Systems 21: Proceedings of the 2008 Conference
Start Page:1937
End Page:1944
Physical Description:8
Audience:Not Specified
Intended Educational Use:No
Abstract / Description: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.
External Publication Status:published
Document Type:Conference-Paper
Communicated by:Holger Fischer
Affiliations:MPI für biologische Kybernetik/Empirical Inference (Dept. Schölkopf)
Identifiers:LOCALID:5465
URL:http://nips.cc/Conferences/2008/
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