Intrinsic dimension estimation of data: an approach based on Grassberger-Procaccia's algorithm

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.


Published in:
Neural Processing Letters, 14, 01, 27-34
Year:
2001
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to appear
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 Record created 2006-03-10, last modified 2018-12-03

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