Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866918368933576704 |
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| author | Zeulin, Nikita Galinina, Olga Balakrishnan, Ravikumar Himayat, Nageen Andreev, Sergey |
| author_facet | Zeulin, Nikita Galinina, Olga Balakrishnan, Ravikumar Himayat, Nageen Andreev, Sergey |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03830 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective Zeulin, Nikita Galinina, Olga Balakrishnan, Ravikumar Himayat, Nageen Andreev, Sergey Machine Learning 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. |
| title | Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.03830 |