NDLI: Bayesian object matching

Content Provider	Springer Nature Link
Author	Klami, Arto
Copyright Year	2013
Abstract	Matching of object refers to the problem of inferring unknown co-occurrence or alignment between observations or samples 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. Given a distance measure, the problem would correspond to a linear assignment problem, the problem of finding a permutation that re-orders samples in one set to minimize the total distance. When no such measure is available, we need to consider more complex solutions. Typical approaches maximize statistical dependency between the two sets, whereas in this work we present a Bayesian solution that builds a joint model for the two sources. We learn a Bayesian canonical correlation analysis model that includes a permutation parameter for re-ordering the samples in one of the sets. We provide both variational and sampling-based inference for approximative Bayesian analysis, and demonstrate on three data sets that the resulting methods outperform the earlier solutions.
Starting Page	225
Ending Page	250
Page Count	26
File Format	PDF
ISSN	08856125
Journal	Machine Learning
Volume Number	92
Issue Number	2-3
e-ISSN	15730565
Language	English
Publisher	Springer US
Publisher Date	2013-04-30
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Canonical correlation analysis Matching Permutation Bayesian analysis Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Computing Methodologies Simulation and Modeling Language Translation and Linguistics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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