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Building Neural Networks on Matrix Manifolds: A Gyrovector Space Approach
Recently, by applying the theory of gyrogroups and gyrovector spaces that is a powerful framework for studying hyperbolic geometry, some works have attempted to build principled generalizations of Euclidean neural networks on matrix manifolds.
GeomNet: A Neural Network Based on Riemannian Geometries of SPD Matrix Space and Cholesky Space for 3D Skeleton-Based Interaction Recognition
In this paper, we propose a novel method for representation and classification of two-person interactions from 3D skeleton sequences.
A neural network based on SPD manifold learning for skeleton-based hand gesture recognition
A new type of layer is proposed for the third stage, based on a variant of stochastic gradient descent on Stiefel manifolds.
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