Next Article in Journal
Optimal Multi-Level Thresholding Based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach
Next Article in Special Issue
New Methods of Entropy-Robust Estimation for Randomized Models under Limited Data
Previous Article in Journal
Large-Sample Asymptotic Approximations for the Sampling and Posterior Distributions of Differential Entropy for Multivariate Normal Distributions
Previous Article in Special Issue
Analysis of Resource and Emission Impacts: An Emergy-Based Multiple Spatial Scale Framework for Urban Ecological and Economic Evaluation
Open AccessArticle

Some Convex Functions Based Measures of Independence and Their Application to Strange Attractor Reconstruction

by Yang Chen 1,* and Kazuyuki Aihara 2,3
1
School of Information Science and Engineering, Southeast University, Nanjing 210096, China
2
Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan
3
Aihara Complexity Modelling Project, ERATO, JST, Saitama 332-0012, Japan
*
Author to whom correspondence should be addressed.
Entropy 2011, 13(4), 820-840; https://doi.org/10.3390/e13040820
Received: 8 February 2011 / Revised: 28 March 2011 / Accepted: 28 March 2011 / Published: 8 April 2011
(This article belongs to the Special Issue Advances in Information Theory)
The classical information-theoretic measures such as the entropy and the mutual information (MI) are widely applicable to many areas in science and engineering. Csiszar generalized the entropy and the MI by using the convex functions. Recently, we proposed the grid occupancy (GO) and the quasientropy (QE) as measures of independence. The QE explicitly includes a convex function in its definition, while the expectation of GO is a subclass of QE. In this paper, we study the effect of different convex functions on GO, QE, and Csiszar’s generalized mutual information (GMI). A quality factor (QF) is proposed to quantify the sharpness of their minima. Using the QF, it is shown that these measures can have sharper minima than the classical MI. Besides, a recursive algorithm for computing GMI, which is a generalization of Fraser and Swinney’s algorithm for computing MI, is proposed. Moreover, we apply GO, QE, and GMI to chaotic time series analysis. It is shown that these measures are good criteria for determining the optimum delay in strange attractor reconstruction. View Full-Text
Keywords: entropy; mutual information; convex function; quality factor; strange attractor; delay-coordinate entropy; mutual information; convex function; quality factor; strange attractor; delay-coordinate
Show Figures

Figure 1

MDPI and ACS Style

Chen, Y.; Aihara, K. Some Convex Functions Based Measures of Independence and Their Application to Strange Attractor Reconstruction. Entropy 2011, 13, 820-840.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop