Estimation of generalized entropies with sample spacing

  • Authors:

  • P. Wachowiak;Renata Smolíková;D. Tourassi;S. Elmaghraby

  • Affiliations:

  • Robarts Research Institute, Imaging Research Laboratories, N6A 5K8, London, ON, Canada;Robarts Research Institute, Imaging Research Laboratories, N6A 5K8, London, ON, Canada;University of Louisville, Department of Computer Engineering and Computer Science, 40292, Louisville, KY, USA;University of Louisville, Department of Computer Engineering and Computer Science, 40292, Louisville, KY, USA

  • Venue:
  • Pattern Analysis & Applications
  • Year:
  • 2005

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Abstract

In addition to the well-known Shannon entropy, generalized entropies, such as the Renyi and Tsallis entropies, are increasingly used in many applications. Entropies are computed by means of nonparametric kernel methods that are commonly used to estimate the density function of empirical data. Generalized entropy estimation techniques for one-dimensional data using sample spacings are proposed. By means of computational experiments, it is shown that these techniques are robust and accurate, compare favorably to the popular Parzen window method for estimating entropies, and, in many cases, require fewer computations than Parzen methods.