Abstract
The promise of spectral clustering is that it can help detect complex shapes and intrinsic manifold structure in large and high dimensional spaces. The price for this promise is the computational cost O(n 3) for computing the eigen-decomposition of the graph Laplacian matrix - so far a necessary subroutine for spectral clustering. In this paper we bypass the eigen-decomposition of the original Laplacian matrix by leveraging the recently introduced Spielman and Teng near-linear time solver for systems of linear equations and random projection. Experiments on several synthetic and real datasets show that the proposed approach has better clustering quality and is faster than the state-of-the-art approximate spectral clustering methods.
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Khoa, N.L.D., Chawla, S. (2012). Large Scale Spectral Clustering Using Resistance Distance and Spielman-Teng Solvers. In: Ganascia, JG., Lenca, P., Petit, JM. (eds) Discovery Science. DS 2012. Lecture Notes in Computer Science(), vol 7569. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33492-4_4
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DOI: https://doi.org/10.1007/978-3-642-33492-4_4
Publisher Name: Springer, Berlin, Heidelberg
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