Graph-Regularized Learning of Gaussian Mixture Models

• URL: http://arxiv.org/abs/2509.13855v1
• Date: Wed, 17 Sep 2025 09:41:26 GMT
• Title: Graph-Regularized Learning of Gaussian Mixture Models
• Authors: Shamsiiat Abdurakhmanova, Alex Jung,
• Abstract summary: We present a graph-regularized learning of Gaussian Mixture Models (GMMs) in distributed settings with heterogeneous and limited local data.<n>The method exploits a provided similarity graph to guide parameter sharing among nodes, avoiding the transfer of raw data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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