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p-exponent and p-leaders, Part I: Negative pointwise regularity

Author

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  • Jaffard, S.
  • Melot, C.
  • Leonarduzzi, R.
  • Wendt, H.
  • Abry, P.
  • Roux, S.G.
  • Torres, M.E.

Abstract

Multifractal analysis aims to characterize signals, functions, images or fields, via the fluctuations of their local regularity along time or space, hence capturing crucial features of their temporal/spatial dynamics. Multifractal analysis is becoming a standard tool in signal and image processing, and is nowadays widely used in numerous applications of different natures. Its common formulation relies on the measure of local regularity via the Hölder exponent, by nature restricted to positive values, and thus to locally bounded functions or signals. It is here proposed to base the quantification of local regularity on p-exponents, a novel local regularity measure potentially taking negative values. First, the theoretical properties of p-exponents are studied in detail. Second, wavelet-based multiscale quantities, the p-leaders, are constructed and shown to permit accurate practical estimation of p-exponents. Exploiting the potential dependence with p, it is also shown how the collection of p-exponents enriches the classification of locally singular behaviors in functions, signals or images. The present contribution is complemented by a companion article developing the p-leader based multifractal formalism associated to p-exponents.

Suggested Citation

  • Jaffard, S. & Melot, C. & Leonarduzzi, R. & Wendt, H. & Abry, P. & Roux, S.G. & Torres, M.E., 2016. "p-exponent and p-leaders, Part I: Negative pointwise regularity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 448(C), pages 300-318.
  • Handle: RePEc:eee:phsmap:v:448:y:2016:i:c:p:300-318
    DOI: 10.1016/j.physa.2015.12.061
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    References listed on IDEAS

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    1. Kantelhardt, Jan W. & Zschiegner, Stephan A. & Koscielny-Bunde, Eva & Havlin, Shlomo & Bunde, Armin & Stanley, H.Eugene, 2002. "Multifractal detrended fluctuation analysis of nonstationary time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 316(1), pages 87-114.
    2. Schumann, Aicko Y. & Kantelhardt, Jan W., 2011. "Multifractal moving average analysis and test of multifractal model with tuned correlations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(14), pages 2637-2654.
    3. Johansen, Anders & Sornette, Didier, 2001. "Finite-time singularity in the dynamics of the world population, economic and financial indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 294(3), pages 465-502.
    4. Gao-Feng Gu & Wei-Xing Zhou, 2010. "Detrending moving average algorithm for multifractals," Papers 1005.0877, arXiv.org, revised Jun 2010.
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    Cited by:

    1. Yun Chen & Huaizhong Li & Liang Hou & Xiangjian Bu & Shaogan Ye & Ding Chen, 2022. "Chatter detection for milling using novel p-leader multifractal features," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 121-135, January.

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