Kernel measures of independence for non-iid data

Xinhua Zhang; Le Song; Arthur Gretton; Alex Smola

Kernel measures of independence for non-iid data

Xinhua Zhang^*, Le Song, Arthur Gretton, Alex Smola

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Citations (Scopus)

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 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.

Original language	English
Title of host publication	Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
Publisher	Neural Information Processing Systems
Pages	1937-1944
Number of pages	8
ISBN (Print)	9781605609492
Publication status	Published - 2009
Event	22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada Duration: 8 Dec 2008 → 11 Dec 2008

Publication series

Name	Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Conference

Conference	22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
Country/Territory	Canada
City	Vancouver, BC
Period	8/12/08 → 11/12/08

Cite this

@inproceedings{43c1db32a77c4928ae4ac89c5ebd75eb,

title = "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 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 = "Xinhua Zhang and Le Song and Arthur Gretton and Alex Smola",

year = "2009",

language = "English",

isbn = "9781605609492",

series = "Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference",

publisher = "Neural Information Processing Systems",

pages = "1937--1944",

booktitle = "Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference",

note = "22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 ; Conference date: 08-12-2008 Through 11-12-2008",

}

Zhang, X, Song, L, Gretton, A & Smola, A 2009, Kernel measures of independence for non-iid data. in Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference, Neural Information Processing Systems, pp. 1937-1944, 22nd Annual Conference on Neural Information Processing Systems, NIPS 2008, Vancouver, BC, Canada, 8/12/08.

Kernel measures of independence for non-iid data. / Zhang, Xinhua; Song, Le; Gretton, Arthur et al.
Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. Neural Information Processing Systems, 2009. p. 1937-1944 (Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Kernel measures of independence for non-iid data

AU - Zhang, Xinhua

AU - Song, Le

AU - Gretton, Arthur

AU - Smola, Alex

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

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M3 - Conference contribution

SN - 9781605609492

T3 - Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

SP - 1937

EP - 1944

BT - Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

PB - Neural Information Processing Systems

T2 - 22nd Annual Conference on Neural Information Processing Systems, NIPS 2008

Y2 - 8 December 2008 through 11 December 2008

ER -

Kernel measures of independence for non-iid data

Abstract

Publication series

Conference

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