Simplicial Cone Shrinking Algorithm for Unmixing Nonnegative Sources

We consider a geometrical approach for solving the Nonnegative Blind Source Separation (N-BSS) problem in the case of noiseless linear instantaneous mixture model. When the sources are nonnegative, the scatter plot of the mixed data is contained in the simplicial cone generated by the mixing matrix. The proposed method, called Simplicial Cone Shrinking Algorithm for Unmixing Nonnegative Sources (SCSA-UNS), estimates the mixing matrix and the sources by finding the Minimum Volume (MV) simplicial cone containing all the mixed data. Simulations on synthetic data shows the efficiency of the proposed method.