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Intrinsic Dimension Estimation of Data: An Approach Based on Grassberger–Procaccia's Algorithm

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Abstract

In this paper the problem of estimating the intrinsic dimension of a data set is investigated. An approach based on the Grassberger–Procaccia's algorithm has been studied. Since this algorithm does not yield accurate measures in high-dimensional data sets, an empirical procedure has been developed. Grassberger–Procaccia's algorithm was tested on two different benchmarks and was compared to a TRN-based method.

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Authors and Affiliations

  1. Elsag spa, Via G. Puccini 2, 16154, Genova, Italy

    Francesco Camastra

  2. Computer Science Department, University of Genoa, Via Dodecaneso 35, 16146, Genova, Italy

    Francesco Camastra

  3. IDIAP – Institut Dalle Molle d'Intelligence Artificielle Perceptive, Rue du Simplon 4, CP592, 1920, Martigny, Switzerland

    Alessandro Vinciarelli

Authors
  1. Francesco Camastra
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  2. Alessandro Vinciarelli
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Camastra, F., Vinciarelli, A. Intrinsic Dimension Estimation of Data: An Approach Based on Grassberger–Procaccia's Algorithm. Neural Processing Letters 14, 27–34 (2001). https://doi.org/10.1023/A:1011326007550

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  • DOI: https://doi.org/10.1023/A:1011326007550

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