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Scalable Spectral Clustering with Weighted PageRank

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8748)

Abstract

In this paper, we propose an accelerated spectral clustering method, using a landmark selection strategy. According to the weighted PageRank algorithm, the most important nodes of the data affinity graph are selected as landmarks. The selected landmarks are provided to a landmark spectral clustering technique to achieve scalable and accurate clustering. In our experiments with two benchmark face and shape image data sets, we examine several landmark selection strategies for scalable spectral clustering that either ignore or consider the topological properties of the data in the affinity graph. Finally, we show that the proposed method outperforms baseline and accelerated spectral clustering methods, in terms of computational cost and clustering accuracy, respectively.

Keywords

Spectral clustering sparse coding databases 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of InformaticsAristotle University of ThessalonikiThessalonikiGreece

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