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arXiv · 2004.06296

Eigen selection in spectral clustering: a theory guided practice

Xiao Han·Xin Tong·Yingying Fan
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Abstract

Based on a Gaussian mixture type model , we derive an eigen selection procedure that improves the usual spectral clustering in high-dimensional settings. Concretely, we derive the asymptotic expansion of the spiked eigenvalues under eigenvalue multiplicity and eigenvalue ratio concentration results, giving rise to the first theory-backed eigen selection procedure in spectral clustering. The resulting eigen-selected spectral clustering (ESSC) algorithm enjoys better stability and compares favorably against canonical alternatives. We demonstrate the advantages of ESSC using extensive simulation and multiple real data studies.

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Xiao Han, Xin Tong, Yingying Fan. 2020-04-14. Eigen selection in spectral clustering: a theory guided practice. https://arxiv.org/abs/2004.06296

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