GitHub repository: yakovdan/ManiFeSt
Primary language: Python
Description: We propose a novel few-sample supervised feature selection (FS) method. It learns class-specific feature space manifolds using multi-feature association kernels. The composite kernel captures differences in learned associations, and a spectral-based FS score is derived using Riemannian geometry.