Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation

S. Liu; J.A. Quinn; M.U. Gutmann; M. Sugiyama

doi:10.1007/978-3-642-40991-2_38

Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation

S. Liu, J.A. Quinn, M.U. Gutmann, M. Sugiyama

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

Abstract / Description of output

We propose a new method for detecting changes in Markov network structure between two sets of samples. Instead of naively fitting two Markov network models separately to the two data sets and figuring out their difference, we directly learn the network structure change by estimating the ratio of Markov network models. This density-ratio formulation naturally allows us to introduce sparsity in the network structure change, which highly contributes to enhancing interpretability. Furthermore, computation of the normalization term, which is a critical computational bottleneck of the naive approach, can be remarkably mitigated.
Through experiments on gene expression and Twitter data analysis, we demonstrate the usefulness of our method.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases
Subtitle of host publication	European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II
Publisher	Springer
Pages	596-611
Number of pages	16
ISBN (Electronic)	978-3-642-40991-2
ISBN (Print)	978-3-642-40990-5
DOIs	https://doi.org/10.1007/978-3-642-40991-2_38
Publication status	Published - Sept 2013

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Berlin Heidelberg
Volume	8189
ISSN (Print)	0302-9743

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Liu2013Accepted author manuscript, 1.78 MB

http://link.springer.com/chapter/10.1007%2F978-3-642-40991-2_38

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Liu, S., Quinn, J. A., Gutmann, M. U., & Sugiyama, M. (2013). Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II (pp. 596-611). (Lecture Notes in Computer Science; Vol. 8189). Springer. https://doi.org/10.1007/978-3-642-40991-2_38

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title = "Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation",

abstract = "We propose a new method for detecting changes in Markov network structure between two sets of samples. Instead of naively fitting two Markov network models separately to the two data sets and figuring out their difference, we directly learn the network structure change by estimating the ratio of Markov network models. This density-ratio formulation naturally allows us to introduce sparsity in the network structure change, which highly contributes to enhancing interpretability. Furthermore, computation of the normalization term, which is a critical computational bottleneck of the naive approach, can be remarkably mitigated.Through experiments on gene expression and Twitter data analysis, we demonstrate the usefulness of our method.",

author = "S. Liu and J.A. Quinn and M.U. Gutmann and M. Sugiyama",

year = "2013",

month = sep,

doi = "10.1007/978-3-642-40991-2_38",

language = "English",

isbn = "978-3-642-40990-5",

series = "Lecture Notes in Computer Science",

publisher = "Springer",

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Liu, S, Quinn, JA, Gutmann, MU & Sugiyama, M 2013, Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. in Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II. Lecture Notes in Computer Science, vol. 8189, Springer, pp. 596-611. https://doi.org/10.1007/978-3-642-40991-2_38

Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. / Liu, S.; Quinn, J.A.; Gutmann, M.U. et al.
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II. Springer, 2013. p. 596-611 (Lecture Notes in Computer Science; Vol. 8189).

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

TY - GEN

T1 - Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation

AU - Liu, S.

AU - Quinn, J.A.

AU - Gutmann, M.U.

AU - Sugiyama, M.

PY - 2013/9

Y1 - 2013/9

N2 - We propose a new method for detecting changes in Markov network structure between two sets of samples. Instead of naively fitting two Markov network models separately to the two data sets and figuring out their difference, we directly learn the network structure change by estimating the ratio of Markov network models. This density-ratio formulation naturally allows us to introduce sparsity in the network structure change, which highly contributes to enhancing interpretability. Furthermore, computation of the normalization term, which is a critical computational bottleneck of the naive approach, can be remarkably mitigated.Through experiments on gene expression and Twitter data analysis, we demonstrate the usefulness of our method.

AB - We propose a new method for detecting changes in Markov network structure between two sets of samples. Instead of naively fitting two Markov network models separately to the two data sets and figuring out their difference, we directly learn the network structure change by estimating the ratio of Markov network models. This density-ratio formulation naturally allows us to introduce sparsity in the network structure change, which highly contributes to enhancing interpretability. Furthermore, computation of the normalization term, which is a critical computational bottleneck of the naive approach, can be remarkably mitigated.Through experiments on gene expression and Twitter data analysis, we demonstrate the usefulness of our method.

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DO - 10.1007/978-3-642-40991-2_38

M3 - Conference contribution

SN - 978-3-642-40990-5

T3 - Lecture Notes in Computer Science

SP - 596

EP - 611

BT - Machine Learning and Knowledge Discovery in Databases

PB - Springer

ER -

Liu S, Quinn JA, Gutmann MU, Sugiyama M. Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part II. Springer. 2013. p. 596-611. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-40991-2_38

Direct Learning of Sparse Changes in Markov Networks by Density Ratio Estimation

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