Improved FastICA algorithm based on symmetic orthogonalization

The convergence of fast independent component analysis (FastICA) algorithm based on the Newton iterative method was depended on initial value. So the different initial values could result in the different convergence speeds. To deal with this problem, this paper is proposed an improved FastICA algorithm based on symmetric orthogonalization. The algorithm selected initial value randomly, and used serial orthogonalization to get the suitable initial separating matrix firstly. Then it used symmetric orthogonalization to get the separating matrix. Finally, it could get the separated signals. Simulation results show that the proposed algorithm has faster convergence speed than the original and another improved FastICA algorithm with the same signal separation accuracy.