Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.csda.2007.09.012 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
possible application pattern,parametric approach,comparative uniform framework,complex system,information theoretic,general evaluation,decomposition technique,typical information theoretic approach,current state,information theoretic approach,statistical decomposition technique,bioinformatics,singular value decomposition,biomedical research,information transfer,composition,mutual information,bipartite network,complexity,negentropy,information,entropy,principal component analysis,blind source separation,independent component analysis | Information theory,Complex system,Singular value decomposition,Parametric model,Decomposition method (constraint satisfaction),Parametric statistics,Statistical theory,Statistics,Mathematics | Journal |
Volume | Issue | ISSN |
52 | 5 | 0167-9473 |
Citations | PageRank | References |
4 | 0.48 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yalcin Tuncer | 1 | 5 | 1.57 |
| Murat M. Tanik | 2 | 222 | 28.08 |
| David B Allison | 3 | 138 | 9.96 |