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Quaternion Neural Networks: State-of-the-Art and Research Challenges

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Intelligent Data Engineering and Automated Learning – IDEAL 2020 (IDEAL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12490))

Abstract

Machine Learning has recently emerged as a new paradigm for processing all types of information. In particular, Artificial Intelligence is attractive to corporations & research institutions as it provides innovative solutions for unsolved problems, & it enjoys a great popularity among the general public. However, despite the fact that Machine Learning offers huge opportunities for the IT industry, Artificial Intelligence technology is still at its infancy, with many issues to be addressed. In this paper, we present a survey of quaternion applications in Neural Networks, one of the most promising research lines in artificial vision which also has a great potential in several other topics. The aim of this paper is to provide a better understanding of the design challenges of Quaternion Neural Networks & identify important research directions in this increasingly important area.

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Notes

  1. 1.

    The DECODA dataset is focused on speech mining methods from spontaneous speech recorded in call-centers. Its many application is to measure the robustness and weak-supervision performance of any model  [4].

  2. 2.

    The TIMIT dataset consists of 4,288 sentences, from 536 different speakers. It contains the audio examples and its transcription  [8].

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Acknowledgments

This research has been supported by the project “Intelligent and sustainable mobility supported by multi-agent systems and edge computing (InEDGEMobility): Towards Sustainable Intelligent Mobility: Blockchain-based framework for IoT Security”, Reference: RTI2018-095390-B-C32, financed by the Spanish Ministry of Science, Innovation and Universities (MCIU), the State Research Agency (AEI) and the European Regional Development Fund (FEDER).

Author information

Authors and Affiliations

  1. BISITE Research Group, University of Salamanca, Edificio Multiusos I+D+i, Calle Espejo s/n, Salamanca, 37007, Spain

    David García-Retuerta, Roberto Casado-Vara, Fernando De la Prieta, Javier Prieto & Juan M. Corchado

  2. University of Salamanca, Department of Applied Mathematics, Calle del Parque 2, 37008, Salamanca, Spain

    Angel Martin-del Rey

  3. University of Salamanca, Institute of Fundamental Physics and Mathematics, Department of Applied Mathematics, Calle del Parque 2, 37008, Salamanca, Spain

    Angel Martin-del Rey

  4. Air Institute, IoT Digital Innovation Hub (Spain), Calle Segunda 4, 37188, Salamanca, Spain

    Juan M. Corchado

Authors
  1. David García-Retuerta
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  2. Roberto Casado-Vara
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  3. Angel Martin-del Rey
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  4. Fernando De la Prieta
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Corresponding author

Correspondence to Roberto Casado-Vara .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Hujun Yin

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García-Retuerta, D., Casado-Vara, R., Martin-del Rey, A., De la Prieta, F., Prieto, J., Corchado, J.M. (2020). Quaternion Neural Networks: State-of-the-Art and Research Challenges. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12490. Springer, Cham. https://doi.org/10.1007/978-3-030-62365-4_43

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  • DOI: https://doi.org/10.1007/978-3-030-62365-4_43

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  • Online ISBN: 978-3-030-62365-4

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