Pydi, Muni Sreenivas and Dukkipati, Ambedkar (2018) On Consistency of Compressive Spectral Clustering. In: IEEE International Symposium on Information Theory (ISIT), JUN 17-22, 2018, Vail, CO, pp. 2102-2106.
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Abstract
Spectral clustering is one of the most popular methods for community detection in graphs. A key step in spectral clustering algorithms is the eigen decomposition of the nxn graph Laplacian matrix to extract its k leading eigenvectors, where k is the desired number of clusters among n objects. This is prohibitively complex to implement for very large datasets. However, it has recently been shown that it is possible to bypass the eigen decomposition by computing an approximate spectral embedding through graph filtering of random signals. In this paper, we analyze the working of spectral clustering performed via graph filtering on the stochastic block model. Specifically, we characterize the effects of sparsity, dimensionality and filter approximation error on the consistency of the algorithm in recovering planted clusters.
Item Type: | Conference Proceedings |
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Series.: | IEEE International Symposium on Information Theory |
Publisher: | IEEE |
Additional Information: | Copy right for this article belong to IEEE |
Keywords: | spectral methods; clustering; stochastic block model |
Department/Centre: | Division of Electrical Sciences > Computer Science & Automation |
Date Deposited: | 24 Nov 2018 14:29 |
Last Modified: | 24 Nov 2018 14:29 |
URI: | http://eprints.iisc.ac.in/id/eprint/61139 |
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