DocumentCode
834732
Title
Estimating the intrinsic dimension of data with a fractal-based method
Author
Camastra, Francesco ; Vinciarelli, Alessandro
Author_Institution
INFM-DISI, Genoa Univ., Italy
Volume
24
Issue
10
fYear
2002
fDate
10/1/2002 12:00:00 AM
Firstpage
1404
Lastpage
1407
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;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/TPAMI.2002.1039212
Filename
1039212
Link To Document