Deflation-Based FastICA With Adaptive Choices of Nonlinearities

Deflation-based FastICA is a popular method for independent component analysis. In the standard deflation-based approach the row vectors of the unmixing matrix are extracted one after another always using the same nonlinearities. In practice the user has to choose the nonlinearities and the efficiency and robustness of the estimation procedure then strongly depends on this choice as well as on the order in which the components are extracted. In this paper we propose a novel adaptive two-stage deflation-based FastICA algorithm that (i) allows one to use different nonlinearities for different components and (ii) optimizes the order in which the components are extracted. Based on a consistent preliminary unmixing matrix estimate and our theoretical results, the algorithm selects in an optimal way the order and the nonlinearities for each component from a finite set of candidates specified by the user. It is also shown that, for each component, the best possible nonlinearity is obtained by using the log-density function. The resulting ICA estimate is affine equivariant with a known asymptotic distribution. The excellent performance of the new procedure is shown with asymptotic efficiency and finite-sample simulation studies.