Graph-Regularized Learning of Gaussian Mixture Models

Add code
Sep 17, 2025
Shamsiiat Abdurakhmanova, Alex Jung
Figure 1 for Graph-Regularized Learning of Gaussian Mixture Models
Figure 2 for Graph-Regularized Learning of Gaussian Mixture Models
Figure 3 for Graph-Regularized Learning of Gaussian Mixture Models
Figure 4 for Graph-Regularized Learning of Gaussian Mixture Models

Share this with someone who'll enjoy it:

Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon

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.

View paper onarxiv icon

Share this with someone who'll enjoy it:

Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon