A Projection-Pursuit-Based Method for Blind Separation of Nonnegative Sources

Abstract

This paper presents a projection pursuit (PP) based method for blind separation of nonnegative sources. First, the available observation matrix is mapped to construct a new mixing model, in which the unaccessible source matrix is normalized to be column-sum-to-one. Then, the PP method is proposed to solve this new model, where the mixing matrix is estimated column by column through tracing the projections to the mapped observations in specified directions, which leads to the recoveryof the sources. The proposed method is much faster than Chan’s method which has similar assumptions to ours, due to the usage of the optimal projection. Also, it is more advantageous inseparating cross-correlated sources than the independence- and uncorrelation-based methods as it does not employ any statistical information of the sources. Furthermore, the new method doesnot require the mixing matrix to be nonnegative. Simulation results demonstrate the superior performance of our method.