Kernel packets (KPs) provide a general framework to construct compactly supported basis functions for Gaussian processes (GPs) driven by stochastic differential equations (SDEs), enabling efficient training and prediction of GP models.
This paper proposes novel Gaussian process models for vector-valued signals on manifolds that are intrinsically defined and account for the geometry of the space, addressing limitations of previous extrinsic approaches.