# Random Features for Grassmannian Kernels

arXiv: https://arxiv.org/abs/2504.21533 · HTML: https://mlsanity.com/paper/2504.21533/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2025.

## Built on by (most cited first, 1 of 1)

- [Random features for Grassmannian kernel approximation with bounded rank-one projections](https://mlsanity.com/paper/2608.04227/) (2026)

## Abstract (arXiv)

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 comparing subspaces. In this work, we propose a sketching-based approach to approximate Grassmannian kernels using random projections. We introduce three variations of kernel approximation, including two that rely on binarised sketches, offering substantial memory gains. We establish theoretical properties of our method in the special case of G(1, n) and extend it to general G(k, n). Experimental validation demonstrates that our sketched kernels closely match the performance of standard Grassmannian kernels while avoiding the need to compute or store the full kernel matrix. Our approach enables scalable Grassmannian-based methods for large-scale applications in machine learning and pattern recognition.