Riemannian online learning

In emerging fields such as machine learning, quantum computing, biomedical imaging, and robotics, data and decisions often exist in curved, non-Euclidean spaces due to physical constraints or underlying symmetries. Riemannian online optimization provides a new framework for handling learning tasks where data arrives sequentially in geometric spaces. This monograph offers a comprehensive overview of online learning over Riemannian manifolds.