%Aigaion2 BibTeX export from Idiap Publications %Wednesday 20 November 2024 07:24:55 PM @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}, }