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The goal of this paper is to show how different machine learning tools on the Riemannian manifold \(\mathcal {P}_d\) of Symmetric Positive Definite (SPD) matrices can be united under a probabilistic framework. For this, we will need several Gaussian distributions defined on \(\mathcal {P}_d\) . We will show how popular classifiers on \(\mathcal {P}_d\) can be reinterpreted as Bayes Classifiers using these Gaussian distributions. These distributions will also be used for outlier detection and dimension reduction. By showing that those distributions are pervasive in the tools used on \(\mathcal {P}_d\) , we allow for other machine learning tools to be extended to \(\mathcal {P}_d\) .

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A Probabilistic View on Riemannian Machine Learning Models for SPD Matrices

  • 会议论文

  • 发表日期：

    2026年

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  • Thibault de Surrel,
  • Florian Yger,
  • Fabien Lotte,
  • Sylvain Chevallier

摘要

The goal of this paper is to show how different machine learning tools on the Riemannian manifold \(\mathcal {P}_d\) of Symmetric Positive Definite (SPD) matrices can be united under a probabilistic framework. For this, we will need several Gaussian distributions defined on \(\mathcal {P}_d\) . We will show how popular classifiers on \(\mathcal {P}_d\) can be reinterpreted as Bayes Classifiers using these Gaussian distributions. These distributions will also be used for outlier detection and dimension reduction. By showing that those distributions are pervasive in the tools used on \(\mathcal {P}_d\) , we allow for other machine learning tools to be extended to \(\mathcal {P}_d\) .