Functional Analytic Learning Team | RIKEN

The Functional Analytic Learning Team focuses on theories and methods from functional analysis and related fields in machine learning, in particular methods based on reproducing kernel Hilbert spaces (RKHS), matrix and operator theory, Riemannian geometry, information geometry, and optimal transport. An important direction is the theoretical formulations and algorithms based on infinite-dimensional geometrical methods, especially in the RKHS setting. The targeted application domains include, but are not limited to, functional data analysis, computer vision, image and signal processing, brain imaging, and brain computer interfaces.

Research Subjects

• Vector-valued Reproducing Kernel Hilbert Spaces
• Geometrical methods in machine learning

Main Research Fields

• Informatics

Related Research Fields

• Mathematical & Physical Sciences
• Intelligent Informatics
• Mathematical Informatics
• Mathematical Analysis

Keywords

• Reproducing kernel Hilbert spaces
• Riemannian geometry
• Information geometry
• Optimal transport
• Gaussian processes