Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective

• URL: http://arxiv.org/abs/2603.03830v1
• Date: Wed, 04 Mar 2026 08:29:07 GMT
• Title: Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective
• Authors: Nikita Zeulin, Olga Galinina, Ravikumar Balakrishnan, Nageen Himayat, Sergey Andreev,
• Abstract summary: Hyperdimensional computing (HDC) is an emerging resource efficient and low-complexity machine learning method.<n>We propose a maximum-margin HDC classifier, which significantly outperforms baseline HDC methods on several benchmark datasets.
• Score: 4.132339396232777
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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