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arXiv · 2601.13930

On spectral clustering under non-isotropic Gaussian mixture models

Kohei Kawamoto·Yuichi Goto·Koji Tsukuda
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Abstract

We evaluate the misclustering probability of a spectral clustering algorithm under a Gaussian mixture model with a general covariance structure. The algorithm partitions the data into two groups based on the sign of the first principal component score. As a corollary of the main result, the clustering procedure is shown to be consistent in a high-dimensional regime.

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Kohei Kawamoto, Yuichi Goto, Koji Tsukuda. 2026-01-20. On spectral clustering under non-isotropic Gaussian mixture models. https://doi.org/10.1016/j.spl.2026.110894

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