Intrinsic Dimension of Data: An Approach based on Grassberger-Procaccia Algorithm

Camastra, F; Vinciarelli, A.

doi:DOI: 10.1023/A:1011326007550

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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.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/20368

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

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