Communication Dans Un Congrès Année : 2025

Generating Correlation Matrices with Graph Structures Using Convex Optimization

Résumé

This work deals with the generation of theoretical correlation matrices with specific sparsity patterns, associated to graph structures. We present a novel approach based on convex optimization, offering greater flexibility compared to existing techniques, notably by controlling the mean of the entry distribution in the generated correlation matrices. This allows for the generation of correlation matrices that better represent realistic data and can be used to benchmark statistical methods for graph inference.

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hal-04963660 , version 1 (24-02-2025)

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  • HAL Id : hal-04963660 , version 1

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Ali Fakhar, Kévin Polisano, Irène Gannaz, Sophie Achard. Generating Correlation Matrices with Graph Structures Using Convex Optimization. IEEE Statistical Signal Processing Workshop (SSP), Jun 2025, Edinbourg, United Kingdom. ⟨hal-04963660⟩
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