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

Accelerated Spectral Clustering Using Graph Filtering Of Random Signals