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)
Full text not currently available from Enlighten.
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 |
University Staff: Request a correction | Enlighten Editors: Update this record
Deposit and Record Details
Deposit and Record Details