Riemannian batch normalization for SPD neural networks

Understand

Covariance matrices have attracted attention for machine learning applications due to their capacity to capture interesting structure in the data.

• The main challenge is that one needs to take into account the particular geometry of the Riemannian manifold of symmetric positive definite (SPD) matrices they belong to.
• In the context of deep networks, several architectures for these matrices have recently been proposed.
• In our article, we introduce a Riemannian batch normalization (batchnorm) algorithm, which generalizes the one used in Euclidean nets.