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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.
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Contributor : Nicolas Tremblay <>
Submitted on : Monday, May 23, 2016 - 3:14:29 PM
Last modification on : Friday, March 6, 2020 - 1:28:58 AM


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


Nicolas Tremblay, Gilles Puy, Rémi Gribonval, Pierre Vandergheynst. Compressive Spectral Clustering. 33rd International Conference on Machine Learning, Jun 2016, New York, United States. ⟨hal-01320214⟩



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