Dissimilarity Measure Machines - Laboratoire d'informatique fondamentale de Marseille Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2018

Dissimilarity Measure Machines

Résumé

This paper presents a dissimilarity-based discriminative framework for learning from data coming in the form of probability distributions. Departing from the use of positive kernel-based methods, we build upon embeddings based on dissimilarities tailored for distribution. We enable this by extending \citet{balcan2008theory}'s theory of learning with similarity functions to the case of distribution-shaped data. Then, we show that several learning guarantees of the dissimilarity still hold when estimated from empirical distributions. Algorithmically, the proposed approach consists in building features from pairwise dissimilarities and in learning a linear decision function in this new feature space. Our experimental results show that this dissimilarity-based approach works better than the so-called support measure machines or the sliced Wasserstein kernel, and that among several dissimilarities including Kullback-Leibler divergence and Maximum Mean Discrepancy, the entropy-regularized Wasserstein distance provides the best compromise between computational efficiency and accuracy.
Fichier principal
Vignette du fichier
WDMM.pdf (1.03 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-01717940 , version 1 (27-02-2018)
hal-01717940 , version 2 (07-11-2018)

Identifiants

Citer

Alain Rakotomamonjy, Abraham Traoré, Maxime Berar, Rémi Flamary, Nicolas Courty, et al.. Dissimilarity Measure Machines. 2018. ⟨hal-01717940v2⟩
901 Consultations
1071 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More