Projets par an
Résumé
Distributed Stochastic Neighbor Embedding (tSNE) is a wellknown dimensionality reduction technique used for the visualization of highdimensional data. However, despite several improvements, tSNE is not wellsuited to handle large datasets. Indeed, for large datasets, the computation time required to obtain the visualizations is still too high to incorporate it in an interactive data exploration process. Since tSNE can be seen as an Nbody problem in physics, we present a new variant of tSNE based on a popular algorithm used to solve the Nbody problem in physics called ParticleMesh (PM). The problem is solved by first computing a potential in space and deriving from it the force exerted on each body. As the potential can be computed efficiently using Fast Fourier Transforms (FFTs), this leads to a significant speed up. The mathematical correspondence between tSNE and PM presented in this work could also lead to other future improvements since more advanced PM algorithms have been developed in physics for decades.
langue originale  Anglais 

titre  International Joint Conference on Neural Networks 
Editeur  IEEE 
Nombre de pages  8 
Les DOIs  
Etat de la publication  Publié  2021 
Série de publications
Nom  2021 International Joint Conference on Neural Networks (IJCNN) 

Empreinte digitale
Examiner les sujets de recherche de « Accelerating tSNE using Fast Fourier Transforms and the ParticleMesh Algorithm from Physics ». Ensemble, ils forment une empreinte digitale unique.Projets
 1 Actif

CÉCI – Consortium des Équipements de Calcul Intensif
CHAMPAGNE, B., Lazzaroni, R., Geuzaine , C., Chatelain, P. & Knaepen, B.
1/01/18 → 31/12/22
Projet: Recherche
Équipement

Plateforme Technologique Calcul Intensif
Benoît Champagne (!!Manager)
Plateforme technologique Calcul intensifEquipement/installations: Plateforme technolgique