# Fast computation of $L^p$ norm-based specialization distances between bodies of evidence

1 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : In a recent paper [1], we introduced a new family of evidential distances in the framework of belief functions. Using specialization matrices as a representation of bodies of evidence, an evidential distance can be obtained by computing the norm of the difference of these matrices. Any matrix norm can be thus used to define a full metric. In particular, it has been shown that the $L^1$ norm-based specialization distance has nice properties. This distance takes into account the structure of focal elements and has a consistent behavior with respect to the conjunctive combination rule. However, if the frame of discernment on which the problem is defined has $n$ elements, then a specialization matrix size is $2^n \times 2^n$. The straightforward formula for computing a specialization distance involves a matrix product which can be consequently highly time consuming. In this article, several faster computation methods are provided for $L^p$ norm-based specialization distances. These methods are proposed for special kinds of mass functions as well as for the general case.
Type de document :
Communication dans un congrès
F. Cuzzolin. thrid international conference on belief functions, Sep 2014, Oxford, United Kingdom. Springer, 8764, pp.422-431, 2014, Lecture Notes in Artificial Intelligence. 〈10.1007/978-3-319-11191-9_46〉

Littérature citée [19 références]

https://hal.inria.fr/hal-01015330
Contributeur : John Klein <>
Soumis le : jeudi 26 juin 2014 - 11:31:53
Dernière modification le : jeudi 11 janvier 2018 - 06:26:40
Document(s) archivé(s) le : vendredi 26 septembre 2014 - 11:50:45

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Mehena Loudahi, John Klein, Jean-Marc Vannobel, Olivier Colot. Fast computation of $L^p$ norm-based specialization distances between bodies of evidence. F. Cuzzolin. thrid international conference on belief functions, Sep 2014, Oxford, United Kingdom. Springer, 8764, pp.422-431, 2014, Lecture Notes in Artificial Intelligence. 〈10.1007/978-3-319-11191-9_46〉. 〈hal-01015330〉

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