L. Jacques

Random features for Grassmannian kernel approximation with bounded rank-one projections

It is shown that inner products in the random feature space approximate well-defined rotation-invariant Grassmannian kernels that depend only on the principal angles between subspaces, which accurately preserve Grassmannian geometry while reducing computation, memory, and storage.