[2604.05337] Individual-heterogeneous sub-Gaussian Mixture Models

Statistics > Machine Learning arXiv:2604.05337 (stat) [Submitted on 7 Apr 2026] Title:Individual-heterogeneous sub-Gaussian Mixture Models Authors:Huan Qing View a PDF of the paper titled Individual-heterogeneous sub-Gaussian Mixture Models, by Huan Qing View PDF HTML (experimental) Abstract:The classical Gaussian mixture model assumes homogeneity within clusters, an assumption that often fails in real-world data where observations naturally exhibit varying scales or intensities. To address this, we introduce the individual-heterogeneous sub-Gaussian mixture model, a flexible framework that assigns each observation its own heterogeneity parameter, thereby explicitly capturing the heterogeneity inherent in practical applications. Built upon this model, we propose an efficient spectral method that provably achieves exact recovery of the true cluster labels under mild separation conditions, even in high-dimensional settings where the number of features far exceeds the number of samples. Numerical experiments on both synthetic and real data demonstrate that our method consistently outperforms existing clustering algorithms, including those designed for classical Gaussian mixture models. Comments: Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG) Cite as: arXiv:2604.05337 [stat.ML]   (or arXiv:2604.05337v1 [stat.ML] for this version)   https://doi.org/10.48550/arXiv.2604.05337 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission histo...