Fast principal component extraction by a homogeneous neural network

On the basis of the concepts of both weighted subspace criterion and information maximization, the paper proposes a weighted information criterion (WINC) for searching for the optimal solution of a homogeneous neural network. We develop two adaptive algorithms based on the WINC for extracting in parallel multiple principal components. Both algorithms are able to provide an adaptive step size which leads to a significant improvement in the learning performance. Furthermore, the recursive least squares version of WINC algorithms has a low computational complexity O(Np), where N is the input vector dimension and p is the number of desired principal components. Since the weighting matrix does not require an accurate value, it facilitates the system design of the WINC algorithm for real applications. Simulation results are provided to illustrate the effectiveness of WINC algorithms for PCA