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Interpreting the Contribution of Sensors in Blind Source Extraction by Means of Shapley Values

Author

Listed:
  • Guilherme Pelegrina

    (UNICAMP - Universidade Estadual de Campinas = University of Campinas)

  • Leonardo Duarte

    (UNICAMP - Universidade Estadual de Campinas = University of Campinas)

  • Michel Grabisch

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

Several practical applications can be formulated as a problem of estimating a source of interest from a set of mixed data collected by different sensors. Although a lot of effort has been done to address the optimization task in signal extraction, there is a lack in the literature on how to evaluate the contribution of each sensor in the extraction process. In this letter, we propose a model-agnostic approach that can be used to interpret both the contribution of each sensor in the estimated source and the interaction effects between them. Our proposal is based on a solution concept from game theory, called Shapley value. Numerical experiments on synthetic and real data attest the use of our proposal in blind source extraction problems.

Suggested Citation

  • Guilherme Pelegrina & Leonardo Duarte & Michel Grabisch, 2023. "Interpreting the Contribution of Sensors in Blind Source Extraction by Means of Shapley Values," Post-Print halshs-04356790, HAL.
    • Handle: RePEc:hal:journl:halshs-04356790
    DOI: 10.1109/LSP.2023.3295759
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-04356790v1
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    References listed on IDEAS

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    1. Xuejun Zhao & Yong Qin & Changbo He & Limin Jia, 2022. "Underdetermined blind source extraction of early vehicle bearing faults based on EMD and kernelized correlation maximization," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 185-201, January.
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