Year: 2021
Type: preprint
Abstract: Dictionary learning is a key tool for representation learning, that explains the data as linear combination of few basic elements. Yet, this analysis is not amenable in the context of graph learning, as graphs usually belong to different metric spaces. We fill this gap by proposing a new online Graph Dictionary Learning approach, which uses the Gromov Wasserstein divergence for the data fitting term. In our work, graphs are encoded through their nodes' pairwise relations and modeled as convex combination of graph atoms, i.e. dictionary elements, estimated thanks to an online stochastic algorithm, which operates on a dataset of unregistered graphs with potentially different number of nodes. Our approach naturally extends to labeled graphs, and is completed by a novel upper bound that can be used as a fast approximation of Gromov Wasserstein in the embedding space. We provide numerical evidences showing the interest of our approach for unsupervised embedding of graph datasets and for online graph subspace estimation and tracking.
Source: arXiv (Cornell University)
Institutions: Université Côte d'Azur, MAASAI: Modèles et algorithmes pour l'intelligence artificielle, École Normale Supérieure de Lyon, École Polytechnique, Centre de Mathématiques Appliquées de l'École polytechnique, Centre National de la Recherche Scientifique, Laboratoire Jean-Alexandre Dieudonné, Université de Bretagne Sud, Institut de Recherche en Informatique et Systèmes Aléatoires
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Agence Nationale de la Recherche
OpenAlex ID: W3131640093