Mini-batch spectral clustering

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations


The cost of computing the spectrum of Laplacian matrices hinders the application of spectral clustering to large data sets. While approximations recover computational tractability, they can potentially affect clustering performance. This paper proposes a practical approach to learn spectral clustering, where the spectrum of the Laplacian is recovered following a constrained optimization problem that we solve using adaptive mini-batch-based stochastic gradient optimization on Stiefel manifolds. Crucially, the proposed approach is formulated so that the memory footprint of the algorithm is low, the cost of each iteration is linear in the number of samples, and convergence to critical points of the objective function is guaranteed. Extensive experimental validation on data sets with up to half a million samples demonstrate its scalability and its ability to outperform state-of-the-art approximate methods to learn spectral clustering for a given computational budget.

Original languageEnglish (US)
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781509061815
StatePublished - Jun 30 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: May 14 2017May 19 2017

Publication series

NameProceedings of the International Joint Conference on Neural Networks


Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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