Volume 5 Number 4 (Apr. 2010)
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JCP 2010 Vol.5(4): 589-597 ISSN: 1796-203X
doi: 10.4304/jcp.5.4.589-597

Dependent Component Analysis: Concepts and Main Algorithms

Rui Li1, Hongwei Li2, and Fasong Wang2
1 School of Sciences, Henan University of Technology, Zhengzhou 450052, P.R.China
2 School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, P.R.China


Abstract—Dependent Component Analysis(DCA) as an extension of Independent Component Analysis(ICA) for Blind Source Separation(BSS) has more applications than ICA and received more and more attentions during the last several years in the study of signal processing and neural networks. After a general and detailed definition of the DCA model is given, the separateness and uniqueness of the DCA model have been discussed in theory. Then, the stateof- art DCA algorithms are overviewed, these methods include multidimensional ICA, variance dependent BSS, subband decomposition ICA, maximum non-Gaussianity method, Wold decomposition method and time-frequency method are constructed for the BSS problem in theories and some simulations of these algorithms are also exhibited for different applications.

Index Terms—Dependent Component Analysis(DCA), Blind Source Separation(BSS), Independent Component Analysis(ICA); Neural Network; Sparse Component Analysis(SCA)

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Cite: Rui Li, Hongwei Li, and Fasong Wang, " Dependent Component Analysis: Concepts and Main Algorithms," Journal of Computers vol. 5, no. 4, pp. 589-597, 2010.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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