Title :
Estimating the intrinsic dimension of data with a fractal-based method
Author :
Camastra, Francesco ; Vinciarelli, Alessandro
Author_Institution :
INFM-DISI, Genoa Univ., Italy
fDate :
10/1/2002 12:00:00 AM
Abstract :
In this paper, the problem of estimating the intrinsic dimension of a data set is investigated. A fractal-based approach using the Grassberger-Procaccia algorithm is proposed. Since the Grassberger-Procaccia algorithm (1983) performs badly on sets of high dimensionality, an empirical procedure that improves the original algorithm has been developed. The procedure has been tested on data sets of known dimensionality and on time series of Santa Fe competition.
Keywords :
fractals; pattern recognition; time series; Santa Fe competition; data intrinsic dimension estimation; fractal-based method; pattern recognition; time series; Chaos; Feature extraction; Fractals; Iron; Neural networks; Neurons; Pattern recognition; Principal component analysis; Statistical learning; Testing;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2002.1039212
