Overparameterized machine learning (ML) methods such as neural networks may be prohibitively resource intensive for devices with limited computational capabilities. Hyperdimensional computing (HDC) is an emerging resource efficient and low-complexity ML method that allows hardware efficient implementations of (re-)training and inference procedures. In this paper, we propose a maximum-margin HDC classifier, which significantly outperforms baseline HDC methods on several benchmark datasets. Our method leverages a formal relation between HDC and support vector machines (SVMs) that we established for the first time. Our findings may inspire novel HDC methods with potentially more hardware-oriented implementations compared to SVMs, thus enabling more efficient learning solutions for various intelligent resource-constrained applications.
@article{arxiv.2603.03830,
title = {Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective},
author = {Nikita Zeulin and Olga Galinina and Ravikumar Balakrishnan and Nageen Himayat and Sergey Andreev},
journal= {arXiv preprint arXiv:2603.03830},
year = {2026}
}
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This work has been submitted to the IEEE for possible publication