Deterministic Hyperdimensional Learning with Rank Refinement (Student Abstract)

Abstract

Hyperdimensional Computing (HDC) represents data as high-dimensional hypervectors that are robust and efficient for learning. Existing methods often rely on pseudo-random hypervector generation, which can suffer from poor orthogonality and high variance across runs, ultimately slowing convergence. These approaches typically require numerous iterations (20– 100) to achieve acceptable accuracy. We propose a method that utilizes deterministic Sobol-based linear projections and rank-based retraining to construct more stable and discriminative hypervectors, thereby reducing class confusion. Unlike pseudo-random initialization, our projections guarantee reproducibility and better coverage of the feature space. As a result, our approach achieves up to 97% accuracy in only 5 iterations. This makes our model up to 20× faster while simultaneously improving accuracy.