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Granular autoencoders: concepts and design

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Abstract

Autoencoders are regarded as one of the key functional components of deep learning architectures. In this study, we augment the well-known architectures of autoencoders by incorporating a concept of information granularity, which gives rise to so-called granular autoencoders. It is demonstrated that information granularity can be sought as an essential design asset whose optimal allocation produces the autoencoder with better representation capabilities. Several protocols of allocation of information granularity are presented and assessed with regard to their abilities to represent the data. Selected examples including those dealing with clustering time series are included.

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Acknowledgments

This project was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, under grant no. (KEP-5-135-39). The authors, therefore, acknowledge with thanks DSR technical and financial support.

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Authors and Affiliations

  1. Electrical and Computer Engineering Department, Faculty of Engineering, King Abdulaziz University, Jidda, Saudi Arabia

    Witold Pedrycz, Rami Al-Hmouz, Abdullah Balamash & Ali Morfeq

  2. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6R 2V4, Canada

    Witold Pedrycz

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Witold Pedrycz

Authors
  1. Witold Pedrycz
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  2. Rami Al-Hmouz
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  3. Abdullah Balamash
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  4. Ali Morfeq
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Corresponding author

Correspondence to Rami Al-Hmouz.

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The authors declare that they have no conflict of interest.

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This article does not contain any studies with human participants performed by any of the authors.

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Communicated by A. Di Nola.

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Pedrycz, W., Al-Hmouz, R., Balamash, A. et al. Granular autoencoders: concepts and design. Soft Comput 23, 9869–9880 (2019). https://doi.org/10.1007/s00500-019-03916-5

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  • DOI: https://doi.org/10.1007/s00500-019-03916-5

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