Few-to-few Cross-domain Object Matching

Aditya Jitta; Arto Klami

Few-to-few Cross-domain Object Matching

Aditya Jitta, Arto Klami

; Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, PMLR 73:176-187, 2017.

Abstract

Cross-domain object matching refers to the task of inferring unknown alignment between objects in two data collections that do not have a shared data representation. In recent years several methods have been proposed for solving the special case that assumes each object is to be paired with exactly one object, resulting in a constrained optimization problem over permutations. A related problem formulation of cluster matching seeks to match a cluster of objects in one data set to a cluster of objects in the other set, which can be considered as many-to-many extension of cross-domain object matching and can be solved without explicit constraints. In this work we study the intermediate region between these two special cases, presenting a range of Bayesian inference algorithms that work also for few-to-few cross-domain object matching problems where constrained optimization is necessary but the optimization domain is broader than just permutations.

Cite this Paper

BibTeX

@InProceedings{pmlr-v73-jitta17a,
  title = 	 {Few-to-few Cross-domain Object Matching},
  author = 	 {Aditya Jitta and Arto Klami},
  booktitle = 	 {Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks},
  pages = 	 {176--187},
  year = 	 {2017},
  editor = 	 {Antti Hyttinen and Joe Suzuki and Brandon Malone},
  volume = 	 {73},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {20--22 Sep},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v73/jitta17a/jitta17a.pdf},
  url = 	 {http://proceedings.mlr.press/v73/jitta17a.html},
  abstract = 	 {  Cross-domain object matching refers to the task of inferring unknown
 alignment between objects in two data collections that do not have a
 shared data representation. In recent years several methods have
 been proposed for solving the special case that assumes each object
 is to be paired with exactly one object, resulting
 in a constrained optimization problem over permutations. A related
 problem formulation of cluster matching seeks to match a cluster of
 objects in one data set to a cluster of objects in the other set,
 which can be considered as many-to-many extension of cross-domain
 object matching and can be solved without explicit constraints.  In
 this work we study the intermediate region between these two special
 cases, presenting a range of Bayesian inference algorithms that work
 also for few-to-few cross-domain object matching problems where
 constrained optimization is necessary but the optimization domain is
 broader than just permutations. }
}

Endnote

%0 Conference Paper
%T Few-to-few Cross-domain Object Matching
%A Aditya Jitta
%A Arto Klami
%B Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks
%C Proceedings of Machine Learning Research	
%D 2017
%E Antti Hyttinen
%E Joe Suzuki
%E Brandon Malone	
%F pmlr-v73-jitta17a
%I PMLR	
%J Proceedings of Machine Learning Research	
%P 176--187
%U http://proceedings.mlr.press
%V 73
%W PMLR
%X   Cross-domain object matching refers to the task of inferring unknown
 alignment between objects in two data collections that do not have a
 shared data representation. In recent years several methods have
 been proposed for solving the special case that assumes each object
 is to be paired with exactly one object, resulting
 in a constrained optimization problem over permutations. A related
 problem formulation of cluster matching seeks to match a cluster of
 objects in one data set to a cluster of objects in the other set,
 which can be considered as many-to-many extension of cross-domain
 object matching and can be solved without explicit constraints.  In
 this work we study the intermediate region between these two special
 cases, presenting a range of Bayesian inference algorithms that work
 also for few-to-few cross-domain object matching problems where
 constrained optimization is necessary but the optimization domain is
 broader than just permutations.

APA

Jitta, A. & Klami, A.. (2017). Few-to-few Cross-domain Object Matching. Proceedings of The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, in PMLR 73:176-187

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