Graph-Regularized Learning of Gaussian Mixture Models

Shamsiiat Abdurakhmanova; Alex Jung

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arXiv · 2509.13855

Graph-Regularized Learning of Gaussian Mixture Models

Shamsiiat Abdurakhmanova·Alex Jung

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Abstract

We present a graph-regularized learning of Gaussian Mixture Models (GMMs) in distributed settings with heterogeneous and limited local data. The method exploits a provided similarity graph to guide parameter sharing among nodes, avoiding the transfer of raw data. The resulting model allows for flexible aggregation of neighbors' parameters and outperforms both centralized and locally trained GMMs in heterogeneous, low-sample regimes.

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Shamsiiat Abdurakhmanova, Alex Jung. 2025-09-17. Graph-Regularized Learning of Gaussian Mixture Models. https://arxiv.org/abs/2509.13855

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