ARTICLE cam01art/IDIAP Intrinsic dimension estimation of data: an approach based on Grassberger-Procaccia's algorithm Camastra, Francesco Vinciarelli, Alessandro EXTERNAL https://publications.idiap.ch/attachments/reports/2000/rr00-33.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/cam00irr Related documents Neural Processing Letters 14 01 27-34 2001 to appear 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. REPORT cam00IRR/IDIAP Intrinsic dimension estimation of data: an approach based on Grassberger-Procaccia's algorithm Camastra, Francesco Vinciarelli, Alessandro EXTERNAL https://publications.idiap.ch/attachments/reports/2000/rr00-33.pdf PUBLIC Idiap-RR-33-2000 2000 IDIAP To appear in Neural Processing Letters 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.