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Is Entropy Suitable to Characterize Data and Signals for Cognitive Informatics?

Is Entropy Suitable to Characterize Data and Signals for Cognitive Informatics?

Withold Kinsner
Copyright: © 2009 |Pages: 24
ISBN13: 9781605661704|ISBN10: 1605661708|ISBN13 Softcover: 9781616925642|EISBN13: 9781605661711
DOI: 10.4018/978-1-60566-170-4.ch002
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MLA

Kinsner, Withold. "Is Entropy Suitable to Characterize Data and Signals for Cognitive Informatics?." Novel Approaches in Cognitive Informatics and Natural Intelligence, edited by Yingxu Wang, IGI Global, 2009, pp. 28-51. https://doi.org/10.4018/978-1-60566-170-4.ch002

APA

Kinsner, W. (2009). Is Entropy Suitable to Characterize Data and Signals for Cognitive Informatics?. In Y. Wang (Ed.), Novel Approaches in Cognitive Informatics and Natural Intelligence (pp. 28-51). IGI Global. https://doi.org/10.4018/978-1-60566-170-4.ch002

Chicago

Kinsner, Withold. "Is Entropy Suitable to Characterize Data and Signals for Cognitive Informatics?." In Novel Approaches in Cognitive Informatics and Natural Intelligence, edited by Yingxu Wang, 28-51. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-170-4.ch002

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Abstract

This chapter provides a review of Shannon and other entropy measures in evaluating the quality of materials used in perception, cognition, and learning processes. Energy-based metrics are not suitable for cognition, as energy itself does not carry information. Instead, morphological (structural and contextual) metrics as well as entropybased multiscale metrics should be considered in cognitive informatics. Appropriate data and signal transformation processes are defined and discussed in the perceptual framework, followed by various classes of information and entropies suitable for characterization of data, signals, and distortion. Other entropies are also described, including the Rényi generalized entropy spectrum, Kolmogorov complexity measure, Kolmogorov-Sinai entropy, and Prigogine entropy for evolutionary dynamical systems. Although such entropy-based measures are suitable for many signals, they are not sufficient for scale-invariant (fractal and multifractal) signals without corresponding complementary multiscale measures.

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