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Hyperseed: Unsupervised Learning With Vector Symbolic Architectures
LuleƄ University of Technology, Sweden.
La Trobe University, Australia.
La Trobe University, Australia.
La Trobe University, Australia.
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2023 (English)In: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 12, no 12, article id e202300141Article in journal (Refereed) Published
Abstract [en]

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 (VSAs) for fast learning of a topology preserving feature map of unlabeled data. It relies on two major operations of VSA, binding and bundling. The algorithmic part of Hyperseed is expressed within the Fourier holographic reduced representations (FHRR) 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 and on illustrative benchmark use cases, IRIS classification, and a language identification task using the $n$ -gram statistics. The results of these experiments confirm the capabilities of Hyperseed and its applications in neuromorphic hardware.

Place, publisher, year, edition, pages
2023. Vol. 12, no 12, article id e202300141
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Computer Sciences
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URN: urn:nbn:se:ri:diva-62510DOI: 10.1109/TNNLS.2022.3211274OAI: oai:DiVA.org:ri-62510DiVA, id: diva2:1729793
Available from: 2023-01-23 Created: 2023-01-23 Last updated: 2024-06-11Bibliographically approved

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Kleyko, Denis

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