SCG - ICA algorithm for Blind Signal Separation

The gradient based algorithms are the most basic independent component analysis (ICA) algorithms, used in Blind signal separation (BSS). Because these algorithms adopt fixed step size, the choice of step size affects the performance and the convergence speed of the algorithm. In this paper, we propose a new algorithm SCG-ICA for blind signal separation. The new algorithm significantly improves the convergence rate of gradient-based blind source separation. The proposed algorithm is based on the Scaled Conjugate Gradient method, which used to optimize the kurtosis contrast function in order to estimate the demixing matrix. The algorithm is robust to local extrema and shows a very high convergence speed in terms of the computational cost required to reach a given source extraction quality, particularly for short data records. The simulations have proved the efficiency and effectiveness of the proposed algorithm. [M. El-Sayed Waheed, H. Ahmed Khalil and O. Farouk Hassan. SCG-ICA algorithm for Blind Signal Separation. Life Sci J 2013;10(2):727-733] (ISSN:1097-8135). http://www.lifesciencesite.com. 101