The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.10 - October (2002 vol.24)
pp: 1404-1407
ABSTRACT
<p><b>Abstract</b>—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 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.</p>
INDEX TERMS
Bayesian information criterion, correlation integral, Grassberger-Procaccia's algorithm, intrinsic dimension, nonlinear principal component analysis, box-counting dimension, fractal dimension, Kolmogorov capacity.
CITATION
Francesco Camastra, Alessandro Vinciarelli, "Estimating the Intrinsic Dimension of Data with a Fractal-Based Method", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.24, no. 10, pp. 1404-1407, October 2002, doi:10.1109/TPAMI.2002.1039212
21 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool