An overview of statistical decomposition techniques applied to complex systems

Tuncer Y., Tanik M. M., Allison D. B.

COMPUTATIONAL STATISTICS & DATA ANALYSIS, cilt.52, sa.5, ss.2292-2310, 2008 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 52 Sayı: 5
  • Basım Tarihi: 2008
  • Doi Numarası: 10.1016/j.csda.2007.09.012
  • Dergi Adı: COMPUTATIONAL STATISTICS & DATA ANALYSIS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2292-2310
  • Anahtar Kelimeler: 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, INDEPENDENT COMPONENT ANALYSIS, MATRIX
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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. (c) 2007 Elsevier B.V. All rights reserved.