Accelerated Spectral Clustering Using Graph Filtering of Random Signals

We build upon recent advances in graph signal processing to propose a faster spectral clustering algorithm. Indeed, classical spectral clustering is based on the computation of the first $k$ eigenvectors of the similarity matrix' Laplacian, whose computation cost, even for sparse matrices, becomes prohibitive for large datasets. We show that we can estimate the spectral clustering distance matrix without computing these eigenvectors: by graph filtering random signals. Also, we take advantage of the stochasticity of these random vectors to estimate the number of clusters $k$. We compare our method to classical spectral clustering on synthetic data, and show that it reaches equal performance while being faster by a factor at least two for large datasets.


Published in:
2016 Ieee International Conference On Acoustics, Speech And Signal Processing Proceedings, 4094-4098
Presented at:
41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2016), Shanghai, China
Year:
2016
Publisher:
New York, Ieee
ISSN:
1520-6149
ISBN:
978-1-4799-9988-0
Keywords:
Laboratories:


Note: PRIVATE


 Record created 2015-09-29, last modified 2018-09-13

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