“Grassmannian kernel”

Abstract: We propose a family of random feature maps for scalable kernel machines defined over low-dimensional subspaces in high dimensions, i.e. over the Grassmannian manifold. This is typically useful in a machine learning context when data classes or clusters are well represented by the span of a few data points.