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Clifford Algebras: A Proposal Towards Improved Image Recognition in Machine Learning

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Ambient Intelligence – Software and Applications (ISAmI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1239))

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Abstract

Machine learning algorithms are designed to learn autonomously to learn general rules from a set of examples. The importance of this task lies in its potential to provide future and past predictions, as well as to improve the interpretability of the data. RGB images have shown themselves to be a challenging topic to neural networks as their 3 dimensions (Red, Green and Blue) have to be processed using mathematical techniques designed for 1-dimensional inputs. However, an implementation of neural networks using Clifford algebras can speed up the processing time and improve the performance, as the resulting network is based on a 4-dimensional space.

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Acknowledgements

This paper has been partially supported by the Salamanca Ciudad de Cultura y Saberes Foundation under the Talent Attraction Programme (CHROMOSOME project).

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Authors and Affiliations

  1. University of Salamanca, Patio de Escuelas Menores, 37008, Salamanca, Spain

    David García-Retuerta

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  1. David García-Retuerta
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Correspondence to David García-Retuerta .

Editor information

Editors and Affiliations

  1. Departamento de Informática, University of Minho, ALGORITMI Center, Braga, Portugal

    Paulo Novais

  2. DIBRIS, University of Genoa, Genoa, Italy

    Gianni Vercelli

  3. Data Management Group, Technical University of Catalonia, Barcelona, Barcelona, Spain

    Josep L. Larriba-Pey

  4. Department Computer Science and Artificial Intelligence, ETS de Ingenierias Informática y de Telecomunicación, University of Granada, Granada, Spain

    Francisco Herrera

  5. BISITE Research Group, University of Salamanca, Salamanca, Salamanca, Spain

    Pablo Chamoso

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García-Retuerta, D. (2021). Clifford Algebras: A Proposal Towards Improved Image Recognition in Machine Learning. In: Novais, P., Vercelli, G., Larriba-Pey, J.L., Herrera, F., Chamoso, P. (eds) Ambient Intelligence – Software and Applications . ISAmI 2020. Advances in Intelligent Systems and Computing, vol 1239. Springer, Cham. https://doi.org/10.1007/978-3-030-58356-9_27

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