%Aigaion2 BibTeX export from Idiap Publications
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@ARTICLE{cam01art,
                      author = {Camastra, Francesco and Vinciarelli, Alessandro},
                    projects = {Idiap},
                       title = {Intrinsic dimension estimation of data: an approach based on {G}rassberger-{P}rocaccia's algorithm},
                     journal = {Neural Processing Letters},
                      volume = {14},
                      number = {01},
                        year = {2001},
                        note = {to appear},
                    crossref = {cam00irr},
                    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.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2000/rr00-33.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/reports/2000/rr00-33.ps.gz},
ipdmembership={vision},
}



crossreferenced publications: 
@TECHREPORT{cam00IRR,
                      author = {Camastra, Francesco and Vinciarelli, Alessandro},
                    projects = {Idiap},
                       title = {Intrinsic dimension estimation of data: an approach based on Grassberger-Procaccia's algorithm},
                        type = {Idiap-RR},
                      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.},
                         pdf = {https://publications.idiap.ch/attachments/reports/2000/rr00-33.pdf},
                  postscript = {ftp://ftp.idiap.ch/pub/reports/2000/rr00-33.ps.gz},
ipdmembership={vision},
}