Learning a Better SPD Network for Signal Classification: A Riemannian Batch Normalization Method

Symmetric positive definite (SPD) matrices have been widely used as Riemannian feature descriptors in various scientific fields, due to their capacity to encode effective manifold-valued representations. Inspired by the architectural principles of Euclidean deep learning, the emerging SPD neural networks have achieved more robust signal classification. Among these advancements, Riemannian batch normalization (RBN) based on the affine-invariant Riemannian metric (AIRM) has emerged as a key technique for enhancing the learning capability of SPD-based networks. Nevertheless, the reliance of singular value decomposition (SVD) makes this metric relatively unstable for the computation of SPD matrices, especially for the ill-conditioned case. To address this limitation, we propose a novel RBN algorithm based on the recently introduced log-Cholesky metric (LCM), which leverages Cholesky decomposition. Unlike AIRM, the LCM offers enhanced numerical stability and allows for more efficient computation. Specifically, the LCM-based Riemannian operators such as Fr $\acute {\mathrm {e}}$ chet mean and parallel transport (PT) are much simpler than those of AIRM, and both have closed forms. Besides, since LCM is the pullback metric from the Cholesky manifold via Cholesky decomposition, the LCM-based RBN on the SPD manifold can be computed in the Cholesky manifold, further boosting the efficiency. Extensive experiments conducted on four benchmarking datasets certify the effectiveness of our proposed algorithm. The source code is now available at: https://github.com/jjscc/CBN.git.