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Online learning the consensus of multiple correspondences between sets.

Moreno-Garc�a, Carlos Francisco; Serratosa, Francesc


Francesc Serratosa


When several subjects solve the assignment problem of two sets, differences on the correspondences computed by these subjects may occur. These differences appear due to several factors. For example, one of the subjects may give more importance to some of the elements’ attributes than another subject. Another factor could be that the assignment problem is computed through a suboptimal algorithm and different non-optimal correspondences can appear. In this paper, we present a consensus methodology to deduct the consensus of several correspondences between two sets. Moreover, we also present an online learning algorithm to deduct some weights that gauge the impact of each initial correspondence on the consensus. In the experimental section, we show the evolution of these parameters together with the evolution of the consensus accuracy. We observe that there is a clear dependence of the learned weights with respect to the quality of the initial correspondences. Moreover, we also observe that in the first iterations of the learning algorithm, the consensus accuracy drastically increases and then stabilises.


MORENO-GARCÍA, C.F. and SERRATOSA, F. 2015. Online learning the consensus of multiple correspondences between sets. Knowledge-based systems [online], 90, pages 49-57. Available from:

Journal Article Type Article
Acceptance Date Sep 28, 2015
Online Publication Date Oct 8, 2015
Publication Date Dec 31, 2015
Deposit Date Feb 5, 2020
Publicly Available Date Feb 5, 2020
Journal Knowledge-based systems
Print ISSN 0950-7051
Electronic ISSN 1872-7409
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 90
Pages 49-57
Keywords Consensus; Learning weights; Correspondence between sets; Linear solver; Hamming distance
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