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.
@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},
address = {},
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. }
}
%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.
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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