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.


Année
2000
Publisher:
IDIAP
Mots-clefs:
Note:
To appear in Neural Processing Letters
Laboratoires:




 Notice créée le 2006-03-10, modifiée le 2018-03-17

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