Other Extensions
Aapo Hyvärinen, Juha Karhunen, Erkki Oja · 2001
In this chapter, the authors present some additional extensions of the basic independent component analysis (ICA) model. First, they discuss the use of prior information on the mixing matrix, especially on its sparseness. Second, they present models that somewhat relax the assumption of the independence of the components. Finally, they show how to adapt some of the basic ICA algorithms to the case where the data is complex-valued instead of real-valued.