Riemannian Networks over Full-Rank Correlation Matrices

arXiv:2605.19073v1 Announce Type: cross Abstract: Representations on the Symmetric Positive Definite (SPD) manifold have garnered significant attention across different applications. In contrast, the manifold of full-rank correlation matrices, a normalized alternative to SPD matrices, remains largely underexplored. This paper introduces Riemannian networks over the correlation manifold, leveraging five recently developed correlation geometries. We systematically extend basic layers, including Multinomial Logistic Regression (MLR), Fully Connected (FC), and convolutional layers, to these geomet

Source: arXiv cs.AI — read the full report at the original publisher.

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