1 citations · 1 across the 5 of their papers we have counts for
5 papers
Random features for Grassmannian kernel approximation with bounded rank-one projections
Rémi Delogne, Laurent Jacques
We propose a family of random feature maps for scalable kernel machines on low-dimensional subspaces, ie on the Grassmannian manifold. Such representations are useful when data cla…
Random Features for Grassmannian Kernels
Rémi Delogne, Laurent Jacques
The Grassmannian manifold G(k, n) serves as a fundamental tool in signal processing, computer vision, and machine learning, where problems often involve classifying, clustering, or…
Signal processing after quadratic random sketching with optical units
Rémi Delogne, Vincent Schellekens, Laurent Daudet +1
Random data sketching (or projection) is now a classical technique enabling, for instance, approximate numerical linear algebra and machine learning algorithms with reduced computa…
Signal processing with optical quadratic random sketches
Rémi Delogne, Vincent Schellekens, Laurent Daudet +1
Random data sketching (or projection) is now a classical technique enabling, for instance, approximate numerical linear algebra and machine learning algorithms with reduced computa…
ROP inception: signal estimation with quadratic random sketching
Rémi Delogne, Vincent Schellekens, Laurent Jacques
Rank-one projections (ROP) of matrices and quadratic random sketching of signals support several data processing and machine learning methods, as well as recent imaging application…