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An overview of statistical decomposition techniques applied to complex systems |
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| Authors: | Yalcin Tuncer Murat M Tanik |
| Affiliation: | a Middle East Technical University, Ankara, Turkey b Ankara University, Ankara, Turkey c Department of Electrical and Computer Engineering, U.A.B. Birmingham, AL 35294-4461, USA d Department of Biostatistics, School of Public Health, U.A.B. Birmingham, Alabama, USA |
| Abstract: | The current state of the art in applied decomposition techniques is summarized within a comparative uniform framework. These techniques are classified by the parametric or information theoretic approaches they adopt. An underlying structural model common to all parametric approaches is outlined. The nature and premises of a typical information theoretic approach are stressed. Some possible application patterns for an information theoretic approach are illustrated. Composition is distinguished from decomposition by pointing out that the former is not a simple reversal of the latter. From the standpoint of application to complex systems, a general evaluation is provided. |
| Keywords: | Bipartite network Blind source separation Complexity Composition Entropy Independent component analysis Information Information transfer Integration Mutual information Negentropy Network component analysis: Principal component analysis Singular value decomposition |
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