GeoEntropy: A measure of complexity and similarity

Authors:
Tuan D. Pham
Affiliations:
School of Engineering and Information Technology, The University of New South Wales, Canberra, ACT 2600, Australia
Venue:
Pattern Recognition
Year:
2010

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Abstract

Measuring the complexity of a pattern expressed either in time or space has been introduced to quantify the information content of the pattern, which can then be applied for classification. Such information measures are particularly useful for the understanding of systems complexity in many fields of sciences, business and engineering. The novel concept of geostatistical entropy (GeoEntropy) as a measure of pattern complexity and similarity is addressed in this paper. It has been experimentally shown that GeoEntropy is an effective algorithm for studying signal predictability and has superior capability of classifying complex bio-patterns.