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 [BibTeX] [Marc21]
Intrinsic dimension estimation of data: an approach based on Grassberger-Procaccia's algorithm
Type of publication: Idiap-RR
Citation: cam00IRR
Number: Idiap-RR-33-2000
Year: 2000
Institution: IDIAP
Note: To appear in Neural Processing Letters
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.
Userfields: ipdmembership={vision},
Keywords:
Projects Idiap
Authors Camastra, Francesco
Vinciarelli, Alessandro
Crossref by cam01art
Added by: [UNK]
Total mark: 0
Attachments
  • rr00-33.pdf
  • rr00-33.ps.gz
Notes