Random matrix-improved kernels for large dimensional spectral clustering

Abstract : Leveraging on recent random matrix advances in the performance analysis of kernel methods for classification and clustering, this article proposes a new family of kernel functions theoretically largely outperforming standard kernels in the context of asymp-totically large and numerous datasets. These kernels are designed to discriminate statistical means and covariances across data classes at a theoretically minimal rate (with respect to data size). Applied to spectral clustering, we demonstrate the validity of our theoretical findings both on synthetic and real-world datasets (here, the popular MNIST database as well as EEG recordings on epileptic patients). Index Terms— Spectral clustering, inner product kernels, random matrix theory.
Type de document :
Communication dans un congrès
2018 IEEE Statistical Signal Processing Workshop (SSP), Jun 2018, Freiburg, Germany. IEEE Statistical Signal Processing Workshop 2018, 〈10.1109/ssp.2018.8450705 〉
Liste complète des métadonnées

Littérature citée [2 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01812009
Contributeur : Hafiz Tiomoko Ali <>
Soumis le : lundi 11 juin 2018 - 10:25:42
Dernière modification le : mercredi 21 novembre 2018 - 12:20:07
Document(s) archivé(s) le : jeudi 13 septembre 2018 - 00:18:51

Fichier

article_update.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Hafiz Tiomoko Ali, Abla Kammoun, Romain Couillet. Random matrix-improved kernels for large dimensional spectral clustering. 2018 IEEE Statistical Signal Processing Workshop (SSP), Jun 2018, Freiburg, Germany. IEEE Statistical Signal Processing Workshop 2018, 〈10.1109/ssp.2018.8450705 〉. 〈hal-01812009〉

Partager

Métriques

Consultations de la notice

230

Téléchargements de fichiers

98