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Kernel measures of independence for non-iid data

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dc.contributor.authorZhang, Xinhua
dc.contributor.authorSong, Le
dc.contributor.authorGretton, Arthur
dc.contributor.authorSmola, Alexander
dc.contributor.editorDaphne Koller
dc.contributor.editorYoshua Bengio
dc.contributor.editorDale Schuurmans
dc.contributor.editorLeon Bottou
dc.contributor.editorAron Culotta
dc.coverage.spatialVancouver Canada
dc.date.accessioned2015-12-10T22:27:47Z
dc.date.createdDecember 8-14 2008
dc.date.issued2008
dc.date.updated2015-12-09T09:43:34Z
dc.description.abstractMany 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.
dc.identifier.urihttp://hdl.handle.net/1885/54355
dc.publisherMIT Press
dc.relation.ispartofseriesConference on Advances in Neural Information Processing Systems (NIPS 2008)
dc.sourceAdvances in Neural Information Processing Systems 21
dc.source.urihttp://nips.cc/Conferences/2008
dc.titleKernel measures of independence for non-iid data
dc.typeConference paper
local.bibliographicCitation.lastpage1944
local.bibliographicCitation.startpage1937
local.contributor.affiliationZhang, Xinhua, College of Engineering and Computer Science, ANU
local.contributor.affiliationSong, Le, Carnegie-Mellon University
local.contributor.affiliationGretton, Arthur, Max Planck Institute for Biological Cybernetics
local.contributor.affiliationSmola, Alexander, College of Engineering and Computer Science, ANU
local.contributor.authoruidZhang, Xinhua, u4263758
local.contributor.authoruidSmola, Alexander, u4039398
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationu8803936xPUB298
local.identifier.doi10.1.1.143.8375&rank=1
local.identifier.scopusID2-s2.0-84863368683
local.type.statusPublished Version

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