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Researchers detail exact recovery for community detection in dependent Gaussian mixture models

This paper investigates the problem of exact recovery for community detection within Gaussian mixture models. The research focuses on scenarios with dependent and heterogeneous Gaussian noise, where the noise covariance matrix can be non-diagonal and even singular. The authors derive sufficient conditions for exact recovery of the maximum likelihood estimator, influenced by a "\Sigma-whitened separation" metric and local comparison inequalities. AI

RANK_REASON This is a research paper published on arXiv detailing a novel statistical method. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

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Researchers detail exact recovery for community detection in dependent Gaussian mixture models

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  1. arXiv stat.ML TIER_1 English(EN) · Zhongyang Li, Sichen Yang ·

    Exact Recovery of Community Detection in dependent Gaussian Mixture Models

    arXiv:2209.14859v3 Announce Type: replace-cross Abstract: We study exact recovery for community detection in a Gaussian mixture model with dependent and heterogeneous Gaussian noise. The noise covariance matrix $\Sigma$ may be non-diagonal and, in the general formulation, singula…