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.
To appear in Neural Processing Letters
Record created on 2006-03-10, modified on 2016-08-08