# Hyperseed: Unsupervised Learning with Vector Symbolic Architectures

arXiv: https://arxiv.org/abs/2110.08343 · HTML: https://mlsanity.com/paper/2110.08343/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 108 references that are in the map.
- First posted on arXiv 15 Oct 2021.

## Builds on (most cited first, 25 of 25)

- [Superposition of many models into one](https://mlsanity.com/paper/1902.05522/) (2019)
- [Blessing of dimensionality: mathematical foundations of the statistical physics of data](https://mlsanity.com/paper/1801.03421/) (2018)
- [In-memory hyperdimensional computing](https://mlsanity.com/paper/1906.01548/) (2019)
- [Computing on Functions Using Randomized Vector Representations](https://mlsanity.com/paper/2109.03429/) (2021)
- [Vector Symbolic Architectures as a Computing Framework for Emerging Hardware](https://mlsanity.com/paper/2106.05268/) (2021)
- [Hyperdimensional computing as a framework for systematic aggregation of image descriptors](https://mlsanity.com/paper/2101.07720/) (2021)
- [Robust High-dimensional Memory-augmented Neural Networks](https://mlsanity.com/paper/2010.01939/) (2020)
- [A theory of sequence indexing and working memory in recurrent neural networks](https://mlsanity.com/paper/1803.00412/) (2018)
- [Variable Binding for Sparse Distributed Representations: Theory and Applications](https://mlsanity.com/paper/2009.06734/) (2020)
- [Robust computation with rhythmic spike patterns](https://mlsanity.com/paper/1901.07718/) (2019)
- [Density Encoding Enables Resource-Efficient Randomly Connected Neural Networks](https://mlsanity.com/paper/1909.09153/) (2019)
- [End to End Binarized Neural Networks for Text Classification](https://mlsanity.com/paper/2010.05223/) (2020)
- [Autoscaling Bloom Filter: Controlling Trade-off Between True and False Positives](https://mlsanity.com/paper/1705.03934/) (2017)
- [Neural Distributed Autoassociative Memories: A Survey](https://mlsanity.com/paper/1709.00848/) (2017)
- [Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springs](https://mlsanity.com/paper/2004.12691/) (2020)
- [Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing](https://mlsanity.com/paper/2106.09821/) (2021)
- [Integer Echo State Networks: Efficient Reservoir Computing for Digital Hardware](https://mlsanity.com/paper/1706.00280/) (2017)
- [HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing enabled Embedding of n-gram Statistics](https://mlsanity.com/paper/2003.01821/) (2020)
- [Commentaries on "Learning Sensorimotor Control with Neuromorphic Sensors: Toward Hyperdimensional Active Perception" [Science Robotics Vol. 4 Issue 30 (2019) 1-10](https://mlsanity.com/paper/2003.11458/) (2020)
- [Prive-HD: Privacy-Preserved Hyperdimensional Computing](https://mlsanity.com/paper/2005.06716/) (2020)
- [Hyperdimensional Computing for Efficient Distributed Classification with Randomized Neural Networks](https://mlsanity.com/paper/2106.00881/) (2021)
- [Cellular Automata Can Reduce Memory Requirements of Collective-State Computing](https://mlsanity.com/paper/2010.03585/) (2020)
- [A 5 μ W Standard Cell Memory-based Configurable Hyperdimensional Computing Accelerator for Always-on Smart Sensing](https://mlsanity.com/paper/2102.02758/) (2021)
- [Task-Projected Hyperdimensional Computing for Multi-Task Learning](https://mlsanity.com/paper/2004.14252/) (2020)
- [The Importance of Balanced Data Sets: Analyzing a Vehicle Trajectory Prediction Model based on Neural Networks and Distributed Representations](https://mlsanity.com/paper/2010.00084/) (2020)

## Built on by (most cited first, 8 of 8)

- [A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges](https://mlsanity.com/paper/2112.15424/) (2021)
- [Unpaired Image Translation via Vector Symbolic Architectures](https://mlsanity.com/paper/2209.02686/) (2022)
- [Generalized Holographic Reduced Representations](https://mlsanity.com/paper/2405.09689/) (2024)
- [Lifelong Intelligence Beyond the Edge using Hyperdimensional Computing](https://mlsanity.com/paper/2403.04759/) (2024)
- [Automated Architecture Search for Brain-inspired Hyperdimensional Computing](https://mlsanity.com/paper/2202.05827/) (2022)
- [Self-Attention Based Semantic Decomposition in Vector Symbolic Architectures](https://mlsanity.com/paper/2403.13218/) (2024)
- [Symbolic Disentangled Representations for Images](https://mlsanity.com/paper/2412.19847/) (2024)
- [Computing with Hypervectors for Efficient Speaker Identification](https://mlsanity.com/paper/2208.13285/) (2022)

## Abstract (arXiv)

Motivated by recent innovations in biologically-inspired neuromorphic hardware, this article presents a novel unsupervised machine learning algorithm named Hyperseed that draws on the principles of Vector Symbolic Architectures (VSA) for fast learning of a topology preserving feature map of unlabelled data. It relies on two major operations of VSA, binding and bundling. The algorithmic part of Hyperseed is expressed within Fourier Holographic Reduced Representations model, which is specifically suited for implementation on spiking neuromorphic hardware. The two primary contributions of the Hyperseed algorithm are, few-shot learning and a learning rule based on single vector operation. These properties are empirically evaluated on synthetic datasets as well as on illustrative benchmark use-cases, IRIS classification, and a language identification task using n-gram statistics. The results of these experiments confirm the capabilities of Hyperseed and its applications in neuromorphic hardware.