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Extreme Machine Learning Architectures Based on Correlation

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Pattern Recognition (MCPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13264))

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Abstract

The relationships between features can guide the architecture of pattern recognition systems. It is the case with images where the spatial relationship of pixels allows to use convolutions to filter images and extract high level features. In this paper, we consider extreme learning machine (ELM) classifiers and propose to modify the selection of the inputs of a hidden unit based on correlation matrix of the input features. The proposed approach is tested on databases of handwritten digits (MNIST and Devanagari). The results show that input selection based on the correlation matrix provides better performance and leads to a sparse representation of the weights in the network.

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Author information

Authors and Affiliations

  1. Department of Computer Science, California State University, Fresno, USA

    Hubert Cecotti

Authors
  1. Hubert Cecotti
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Correspondence to Hubert Cecotti .

Editor information

Editors and Affiliations

  1. Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Mexico

    Osslan Osiris Vergara-Villegas

  2. Universidad Autónoma de Ciudad Juárez, Ciudad Juárez, Mexico

    Vianey Guadalupe Cruz-Sánchez

  3. Instituto Politécnico Nacional, Mexico City, Mexico

    Juan Humberto Sossa-Azuela

  4. Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico

    Jesús Ariel Carrasco-Ochoa

  5. Instituto Nacional de Astrofísica, Óptica y Electrónica, Puebla, Mexico

    José Francisco Martínez-Trinidad

  6. Autonomous University of Puebla, Puebla, Mexico

    José Arturo Olvera-López

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Cecotti, H. (2022). Extreme Machine Learning Architectures Based on Correlation. In: Vergara-Villegas, O.O., Cruz-Sánchez, V.G., Sossa-Azuela, J.H., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F., Olvera-López, J.A. (eds) Pattern Recognition. MCPR 2022. Lecture Notes in Computer Science, vol 13264. Springer, Cham. https://doi.org/10.1007/978-3-031-07750-0_13

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  • DOI: https://doi.org/10.1007/978-3-031-07750-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07749-4

  • Online ISBN: 978-3-031-07750-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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