Overview and Comparison of Basic ICA Methods
Aapo Hyvärinen, Juha Karhunen, Erkki Oja · 2001
In the preceding chapters, the authors introduced several different estimation principles and algorithms for independent component analysis (ICA). In this chapter, they provide an overview of these methods. First, they show that all these estimation principles are intimately connected, and the main choices are between cumulant-based vs. negentropy/likelihood-based estimation methods, and between one-unit vs. multi-unit methods. They compare the algorithms experimentally, and show that the main choice here is between on-line (adaptive) gradient algorithms vs. fast batch fixed-point algorithms. At the end of the chapter, they provide a short summary of basic ICA estimation.