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arXiv · 2007.11416

Spectral Clustering using Eigenspectrum Shape Based Nystrom Sampling

Djallel Bouneffouf
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Abstract

Spectral clustering has shown a superior performance in analyzing the cluster structure. However, its computational complexity limits its application in analyzing large-scale data. To address this problem, many low-rank matrix approximating algorithms are proposed, including the Nystrom method - an approach with proven approximate error bounds. There are several algorithms that provide recipes to construct Nystrom approximations with variable accuracies and computing times. This paper proposes a scalable Nystrom-based clustering algorithm with a new sampling procedure, Centroid Minimum Sum of Squared Similarities (CMS3), and a heuristic on when to use it. Our heuristic depends on the eigen spectrum shape of the dataset, and yields competitive low-rank approximations in test datasets compared to the other state-of-the-art methods

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Djallel Bouneffouf. 2020-07-21. Spectral Clustering using Eigenspectrum Shape Based Nystrom Sampling. https://arxiv.org/abs/2007.11416

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