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

Loudahi, Mehena; Klein, John; Vannobel, Jean-Marc; Colot, Olivier

Type de document :

Communication dans un congrès avec actes

DOI :

10.1007/978-3-319-11191-9_46

Titre :

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

Auteur(s) :

Loudahi, Mehena [Auteur]
LAGIS-SI
Klein, John [Auteur]

LAGIS-SI
Vannobel, Jean-Marc [Auteur]

LAGIS-SI
Colot, Olivier [Auteur]

LAGIS-SI

Éditeur(s) ou directeur(s) scientifique(s) :

F. Cuzzolin

Titre de la manifestation scientifique :

thrid international conference on belief functions

Ville :

Oxford

Pays :

Royaume-Uni

Date de début de la manifestation scientifique :

2014-09-26

Titre de la revue :

Lecture Notes in Artificial Intelligence

Éditeur :

Springer

Date de publication :

2014-06-26

Discipline(s) HAL :

Informatique [cs]/Intelligence artificielle [cs.AI]

Résumé en anglais : [en]

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 ...
Lire la suite >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.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :