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

Camastra, F. and Vinciarelli, A. (2001) Intrinsic dimension estimation of data: an approach based on Grassberger-Procaccia's algorithm. Neural Processing Letters, 14(1), pp. 27-34. (doi:10.1023/A:1011326007550)

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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.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Vinciarelli, Professor Alessandro
Authors: Camastra, F., and Vinciarelli, A.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Neural Processing Letters
ISSN:1370-4621
ISSN (Online):1573-773X

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