Local Convergence Analysis of FastICA and Related Algorithms

Hao Shen, M. Kleinsteuber, K. Huper
2008 IEEE Transactions on Neural Networks  
The FastICA algorithm is one of the most prominent methods to solve the problem of linear independent component analysis (ICA). Although there have been several attempts to prove local convergence properties of FastICA, rigorous analysis is still missing in the community. The major difficulty of analysis is because of the well-known sign-flipping phenomenon of FastICA, which causes the discontinuity of the corresponding FastICA map on the unit sphere. In this paper, by using the concept of
more » ... the concept of principal fiber bundles, FastICA is proven to be locally quadratically convergent to a correct separation. Higher order local convergence properties of FastICA are also investigated in the framework of a scalar shift strategy. Moreover, as a parallelized version of FastICA, the so-called QR FastICA algorithm, which employs the QR decomposition (Gram-Schmidt orthonormalization process) instead of the polar decomposition, is shown to share similar local convergence properties with the original FastICA. Index Terms-Approximate Newton method, convergence analysis, FastICA, independent component analysis (ICA), principal fiber bundle.
doi:10.1109/tnn.2007.915117 pmid:18541502 fatcat:opn6lofanfe6zlq45jzqagsxli