Work

On the Consistency of Spectral Clustering under Generalized Gaussian Mixture Models

Year: 2026

Type: preprint

Abstract: Spectral clustering is a widely used clustering method with strong empirical performance and favorable computational scalability. Existing theory has established its consistency and risk guarantees under Gaussian and related mixture models, primarily in settings with relatively simple latent structure. In this paper, we study spectral clustering under a semiparametric Gaussian mixture model, where the observed data become Gaussian-mixture distributed only after unknown monotone coordinate-wise transformations. We derive non-asymptotic bounds for its cluster recovery error. As a byproduct of the theory, we show that spectral clustering provides a theoretically justified initialization for a broad class of high-dimensional mixture-based learning procedures.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.33944

Authors: Zhang, Lyuou, Wang, Boxiang, Zhou, Wen, Zou, Hui

Institutions: University of Iowa, Shanghai University of Finance and Economics, New York University, Johns Hopkins University

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7223917298

Cites: 1

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Related to: 10