Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 27 of its 67 references that are in the map. First posted on arXiv 4 Mar 2026.
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27 of 67 references.
References · 27
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15 more
- 2021Hyperdimensional Feature Fusion for Out-Of-Distribution Detectioncited by 13
- 2022HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computingcited by 13
- 2021Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computingcited by 12
- 2020Prive-HD: Privacy-Preserved Hyperdimensional Computingcited by 7
- 2020Cellular Automata Can Reduce Memory Requirements of Collective-State Computingcited by 6
- 2024NeuroHash: A Hyperdimensional Neuro-Symbolic Framework for Spatially-Aware Image Hashing and Retrievalcited by 6
- 2025Hyperdimensional Uncertainty Quantification for Multimodal Uncertainty Fusion in Autonomous Vehicles Perceptioncited by 6
- 2023An Encoding Framework for Binarized Images using HyperDimensional Computingcited by 5
- 2024Laplace-HDC: Understanding the geometry of binary hyperdimensional computingcited by 4
- 2023Resource-Efficient Federated Hyperdimensional Computingcited by 3
- 2024MicroHD: An Accuracy-Driven Optimization of Hyperdimensional Computing Algorithms for TinyML systemscited by 3
- 2024Efficient Open Modification Spectral Library Searching in High-Dimensional Space with Multi-Level-Cell Memorycited by 3
- 2025HD-CB: The First Exploration of Hyperdimensional Computing for Contextual Bandits Problemscited by 2
- 2024On Design Choices in Similarity-Preserving Sparse Randomized Embeddingscited by 1
- 2025MEMHD: Memory-Efficient Multi-Centroid Hyperdimensional Computing for Fully-Utilized In-Memory Computing Architecturescited by 1
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