Online Kernel-Based Graph Topology Identification with Partial-Derivative-Imposed Sparsity - ANR - Agence nationale de la recherche Accéder directement au contenu
Communication Dans Un Congrès Année : 2021

Online Kernel-Based Graph Topology Identification with Partial-Derivative-Imposed Sparsity

Ricardo Augusto Borsoi
Cédric Richard

Résumé

In many applications, such as brain network connectivity or shopping recommendations, the underlying graph explaining the different interactions between participating agents is unknown. Moreover, many of these interactions may be based on nonlinear relationships, rendering the topology inference problem more complex. This paper presents a new topology inference method that estimates a possibly directed adjacency matrix in an online manner. In contrast to previous approaches which are based on additive models, the proposed model is able to explain general nonlinear interactions between the agents. Partial-derivative-imposed sparsity is implemented, while reproducing kernels are used to model nonlinearities. The impact of the increasing number of data points is alleviated by using dictionaries of kernel functions. A comparison with a previously developed method showcases the generality of the new model.
Fichier principal
Vignette du fichier
moscu2020online.pdf (1.36 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03347367 , version 1 (17-09-2021)

Identifiants

Citer

Mircea Moscu, Ricardo Augusto Borsoi, Cédric Richard. Online Kernel-Based Graph Topology Identification with Partial-Derivative-Imposed Sparsity. 2020 28th European Signal Processing Conference (EUSIPCO), Jan 2021, Amsterdam, France. pp.2190-2194, ⟨10.23919/Eusipco47968.2020.9287624⟩. ⟨hal-03347367⟩
22 Consultations
66 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More