Large Scale Spectral Clustering Using Sparse Representation Based on Hubness

Abstract

Spectral clustering has been shown to be more effective than most of the traditional clustering algorithms. However, the heavy computational cost of spectral clustering limits its applicability to large-scale clustering problems. To perform spectral clustering on large datasets, in this paper, we propose an accelerated spectral clustering method based on sparse presentation where each data point is presented as sparse linear combinations of a part of representative data points. The hubs that appear frequently in the nearest neighbor lists of other data points are selected as the representative data points, by which a proper spectral embedding is constructed. Taking advantage of the topological property of hubs, the proposed method is able to achieve scalable and accurate clustering results. We evaluated the proposed method on both synthetic and real-world datasets to show its effectiveness in comparison to the existing related methods.