Compressive Spectral Clustering

Abstract : Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a feature vector for each object, and run k-means on these features to separate objects into k classes. Each of these three steps becomes computationally intensive for large N and/or k. We propose to speed up the last two steps based on recent results in the emerging field of graph signal processing: graph filtering of random signals , and random sampling of bandlimited graph signals. We prove that our method, with a gain in computation time that can reach several orders of magnitude, is in fact an approximation of spectral clustering, for which we are able to control the error. We test the performance of our method on artificial and real-world network data.
Type de document :
Communication dans un congrès
33rd International Conference on Machine Learning, Jun 2016, New York, United States. Proceedings of the 33rd International Conference on Machine Learning (ICML)
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-01320214
Contributeur : Nicolas Tremblay <>
Soumis le : lundi 23 mai 2016 - 15:14:29
Dernière modification le : lundi 2 octobre 2017 - 16:06:02

Identifiants

  • HAL Id : hal-01320214, version 1
  • ARXIV : 1602.02018

Citation

Nicolas Tremblay, Gilles Puy, Rémi Gribonval, Pierre Vandergheynst. Compressive Spectral Clustering. 33rd International Conference on Machine Learning, Jun 2016, New York, United States. Proceedings of the 33rd International Conference on Machine Learning (ICML). 〈hal-01320214〉

Partager

Métriques

Consultations de
la notice

868

Téléchargements du document

360