Dependent Component Analysis: Concepts and Main Algorithms

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 state-of-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.