Unit LeaderHa Quang Minh
Postdoctoral ResearcherJean Carlo Guella
The Functional Analytic Learning Unit focuses on functional analytic and geometrical methods in machine learning, in particular methods based on Riemannian geometry, matrix and operator theory, and vector-valued Reproducing Kernel Hilbert Spaces (RKHS). An important direction is the theoretical formulations and algorithms based on the geometry of positive definite operators, especially RKHS covariance operators. 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.
Geometrical methods in machine learning
[Poster] FY2018 Research Results