Related Experiment Video
Updated: May 28, 2026

Multimodal Nonlinear Hyperspectral Chemical Imaging Using Line-Scanning Vibrational Sum-Frequency Generation Microscopy
Published on: December 1, 2023
Riemannian-gradient-based learning on the complex matrix-hypersphere
1Dipartimento di Ingegneria dell’Informazione, Facoltà di Ingegneria, Università Politecnica delle Marche, Via Brecce Bianche, Ancona I-60131, Italy. s.fiori@univpm.it
Abstract:
This brief tackles the problem of learning over the complex-valued matrix-hypersphere S(α)(n,p)(C). The developed learning theory is formulated in terms of Riemannian-gradient-based optimization of a regular criterion function and is implemented by a geodesic-stepping method. The stepping method is equipped with a geodesic-search sub-algorithm to compute the optimal learning stepsize at any step. Numerical results show the effectiveness of the developed learning method and of its implementation.
Related Concept Videos
Hyperbolic and Inverse Hyperbolic Functions: Problem Solving
Inverse Hyperbolic Functions and Their Derivatives
Geometry of Hyperbolas
Gauss's Law: Spherical Symmetry
Hyperbolic Functions
Complex Numbers