Characterizing attractors using local intrinsic dimension via higher-order statistics
A. Passamante and Mary Eileen Farrell
Phys. Rev. A 43, 5268 (1991) - Published 1 May, 1991
Often it is required to make dimension estimates on data corrupted with moderate amounts of additive noise. The dimension of interest in this paper is the local intrinsic dimension of the attractor in the phase space. In this work we present a technique to estimate the local intrinsic dimension using higher-order statistical information, in particular, using statistical information beyond what is contained in methods based on the covariance. The average higher-order local intrinsic dimension (〈〉) is calculated from noisy data, the additive noise of which may be correlated and nonwhite in nature. With use of noisy data samples, the higher-order local intrinsic dimension (HOLID) is calculated from analysis of a higher-order cumulant matrix formed within local attractor regions. The local rank of the HOLID matrix is computed using the method of singular-value decomposition and the singular values so formed are then separated into signal components and noise components by applying a direct threshold. The local rank is an integer that varies with position on the attractor. To obtain 〈〉 an average of the available local HOLID’s is calculated. Results of the 〈〉 compare favorably with those of the covariance formulation 〈〉 for finding the local intrinsic dimension. Also presented, for comparison purposes, with these results are the correlation dimension for some known chaotic cases since they are so widely used. Tests performed with different signal and noise types and at various signal-to-noise ratios have indicated that 〈〉 can yield more accurate dimension information in the presence of correlated noise, but more data samples must be processed.