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On Consistency of Compressive Spectral Clustering

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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Official URL: http://dx.doi.org/10.1109/ISIT.2018.8437911


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
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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