# LeHDC: Learning-Based Hyperdimensional Computing Classifier

arXiv: https://arxiv.org/abs/2203.09680 · HTML: https://mlsanity.com/paper/2203.09680/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 18 Mar 2022.

## Builds on (most cited first, 6 of 6)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Classification using Hyperdimensional Computing: A Review](https://mlsanity.com/paper/2004.11204/) (2020)
- [In-memory hyperdimensional computing](https://mlsanity.com/paper/1906.01548/) (2019)
- [How Do Adam and Training Strategies Help BNNs Optimization?](https://mlsanity.com/paper/2106.11309/) (2021)
- [A Theoretical Perspective on Hyperdimensional Computing](https://mlsanity.com/paper/2010.07426/) (2020)
- [Understanding Hyperdimensional Computing for Parallel Single-Pass Learning](https://mlsanity.com/paper/2202.04805/) (2022)

## Built on by (most cited first, 17 of 17)

- [Hyperdimensional Computing vs. Neural Networks: Comparing Architecture and Learning Process](https://mlsanity.com/paper/2207.12932/) (2022)
- [Learning from Hypervectors: A Survey on Hypervector Encoding](https://mlsanity.com/paper/2308.00685/) (2023)
- [NeuroHash: A Hyperdimensional Neuro-Symbolic Framework for Spatially-Aware Image Hashing and Retrieval](https://mlsanity.com/paper/2404.11025/) (2024)
- [An Encoding Framework for Binarized Images using HyperDimensional Computing](https://mlsanity.com/paper/2312.00454/) (2023)
- [MetaLDC: Meta Learning of Low-Dimensional Computing Classifiers for Fast On-Device Adaption](https://mlsanity.com/paper/2302.12347/) (2023)
- [Training a HyperDimensional Computing Classifier using a Threshold on its Confidence](https://mlsanity.com/paper/2305.19007/) (2023)
- [Efficient Hyperdimensional Computing](https://mlsanity.com/paper/2301.10902/) (2023)
- [HyperCam: Low-Power Onboard Computer Vision for IoT Cameras](https://mlsanity.com/paper/2501.10547/) (2025)
- [MEMHD: Memory-Efficient Multi-Centroid Hyperdimensional Computing for Fully-Utilized In-Memory Computing Architectures](https://mlsanity.com/paper/2502.07834/) (2025)
- [Towards Vector Optimization on Low-Dimensional Vector Symbolic Architecture](https://mlsanity.com/paper/2502.14075/) (2025)
- [THDC: Training Hyperdimensional Computing Models with Backpropagation](https://mlsanity.com/paper/2602.00116/) (2026)
- [Large-Margin Hyperdimensional Computing: A Learning-Theoretical Perspective](https://mlsanity.com/paper/2603.03830/) (2026)
- [NeuroHD-RA: Neural-distilled Hyperdimensional Model with Rhythm Alignment](https://mlsanity.com/paper/2507.14184/) (2025)
- [BiHDTrans: binary hyperdimensional transformer for efficient multivariate time series classification](https://mlsanity.com/paper/2509.24425/) (2025)
- [Primitive-Driven Acceleration of Hyperdimensional Computing for Real-Time Image Classification](https://mlsanity.com/paper/2601.20061/) (2026)
- [QeHDC: Hyperdimensional Computing based on Quantum-enhanced binding and SuperClass Construction](https://mlsanity.com/paper/2606.22421/) (2026)
- [Phase-HDC: Replacing Optimizer History with Gradient Thresholds in Discrete Phase Learning](https://mlsanity.com/paper/2610.10630/) (2026)

## Abstract (arXiv)

Thanks to the tiny storage and efficient execution, hyperdimensional Computing (HDC) is emerging as a lightweight learning framework on resource-constrained hardware. Nonetheless, the existing HDC training relies on various heuristic methods, significantly limiting their inference accuracy. In this paper, we propose a new HDC framework, called LeHDC, which leverages a principled learning approach to improve the model accuracy. Concretely, LeHDC maps the existing HDC framework into an equivalent Binary Neural Network architecture, and employs a corresponding training strategy to minimize the training loss. Experimental validation shows that LeHDC outperforms previous HDC training strategies and can improve on average the inference accuracy over 15% compared to the baseline HDC.