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On Tsallis extropy with an application to pattern recognition

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  • Balakrishnan, Narayanaswamy
  • Buono, Francesco
  • Longobardi, Maria

Abstract

Recently, a new measure of information called extropy has been introduced by Lad, Sanfilippo and Agrò as the dual version of Shannon entropy. In the literature, Tsallis introduced a measure for a discrete random variable, named Tsallis entropy, as a generalization of Boltzmann–Gibbs statistics. In this work, a new measure of discrimination, called Tsallis extropy, is introduced and some of its properties are then discussed. The relation between Tsallis extropy and entropy is given and some bounds are also presented. Finally, an application of this extropy to pattern recognition is demonstrated.

Suggested Citation

  • Balakrishnan, Narayanaswamy & Buono, Francesco & Longobardi, Maria, 2022. "On Tsallis extropy with an application to pattern recognition," Statistics & Probability Letters, Elsevier, vol. 180(C).
    • Handle: RePEc:eee:stapro:v:180:y:2022:i:c:s0167715221002030
    DOI: 10.1016/j.spl.2021.109241
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    References listed on IDEAS

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    Cited by:

    1. Yu, Zihan & Deng, Yong, 2022. "Derive power law distribution with maximum Deng entropy," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    2. Li, Siran & Xiao, Fuyuan, 2023. "Normal distribution based on maximum Deng entropy," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).

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