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EnglishIn 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.
| Titolo: | Intrinsic Dimension of Data: An Approach based on Grassberger-Procaccia Algorithm | |
| Autori: | ||
| Data di pubblicazione: | 2001 | |
| Rivista: | ||
| 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. | |
| Handle: | http://hdl.handle.net/11367/20368 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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