Variational Bayesian Matching

Arto Klami
Proceedings of the Asian Conference on Machine Learning, PMLR 25:205-220, 2012.

Abstract

Matching of samples refers to the problem of inferring unknown co-occurrence or alignment between observations in two data sets. Given two sets of equally many samples, the task is to find for each sample a representative sample in the other set, without prior knowledge on a distance measure between the sets. Recently a few alternative solutions have been suggested, based on maximization of joint likelihood or various measures of between-data statistical dependency. In this work we present an variational Bayesian solution for the problem, learning a Bayesian canonical correlation analysis model with a permutation parameter for re-ordering the samples in one of the sets. We approximate the posterior over the permutations, and demonstrate that the resulting matching algorithm clearly outperforms all of the earlier solutions.

Cite this Paper

BibTeX
@InProceedings{pmlr-v25-klami12, title = {Variational Bayesian Matching}, author = {Klami, Arto}, booktitle = {Proceedings of the Asian Conference on Machine Learning}, pages = {205--220}, year = {2012}, editor = {Hoi, Steven C. H. and Buntine, Wray}, volume = {25}, series = {Proceedings of Machine Learning Research}, address = {Singapore Management University, Singapore}, month = {04--06 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v25/klami12/klami12.pdf}, url = {https://proceedings.mlr.press/v25/klami12.html}, abstract = {Matching of samples refers to the problem of inferring unknown co-occurrence or alignment between observations in two data sets. Given two sets of equally many samples, the task is to find for each sample a representative sample in the other set, without prior knowledge on a distance measure between the sets. Recently a few alternative solutions have been suggested, based on maximization of joint likelihood or various measures of between-data statistical dependency. In this work we present an variational Bayesian solution for the problem, learning a Bayesian canonical correlation analysis model with a permutation parameter for re-ordering the samples in one of the sets. We approximate the posterior over the permutations, and demonstrate that the resulting matching algorithm clearly outperforms all of the earlier solutions.} }
Endnote
%0 Conference Paper %T Variational Bayesian Matching %A Arto Klami %B Proceedings of the Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2012 %E Steven C. H. Hoi %E Wray Buntine %F pmlr-v25-klami12 %I PMLR %P 205--220 %U https://proceedings.mlr.press/v25/klami12.html %V 25 %X Matching of samples refers to the problem of inferring unknown co-occurrence or alignment between observations in two data sets. Given two sets of equally many samples, the task is to find for each sample a representative sample in the other set, without prior knowledge on a distance measure between the sets. Recently a few alternative solutions have been suggested, based on maximization of joint likelihood or various measures of between-data statistical dependency. In this work we present an variational Bayesian solution for the problem, learning a Bayesian canonical correlation analysis model with a permutation parameter for re-ordering the samples in one of the sets. We approximate the posterior over the permutations, and demonstrate that the resulting matching algorithm clearly outperforms all of the earlier solutions.
RIS
TY - CPAPER TI - Variational Bayesian Matching AU - Arto Klami BT - Proceedings of the Asian Conference on Machine Learning DA - 2012/11/17 ED - Steven C. H. Hoi ED - Wray Buntine ID - pmlr-v25-klami12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 25 SP - 205 EP - 220 L1 - http://proceedings.mlr.press/v25/klami12/klami12.pdf UR - https://proceedings.mlr.press/v25/klami12.html AB - Matching of samples refers to the problem of inferring unknown co-occurrence or alignment between observations in two data sets. Given two sets of equally many samples, the task is to find for each sample a representative sample in the other set, without prior knowledge on a distance measure between the sets. Recently a few alternative solutions have been suggested, based on maximization of joint likelihood or various measures of between-data statistical dependency. In this work we present an variational Bayesian solution for the problem, learning a Bayesian canonical correlation analysis model with a permutation parameter for re-ordering the samples in one of the sets. We approximate the posterior over the permutations, and demonstrate that the resulting matching algorithm clearly outperforms all of the earlier solutions. ER -
APA
Klami, A.. (2012). Variational Bayesian Matching. Proceedings of the Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 25:205-220 Available from https://proceedings.mlr.press/v25/klami12.html.

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