Extending NMF to blindly separate linear-quadratic mixtures of uncorrelated sources
Shahram Hosseini, Yannick Deville, Leonardo Tomazeli Duarte, Ahmed Selloum · 2016
This paper proposes a new constrained method, based on nonnegative matrix factorization, for blindly separating linear-quadratic (LQ) mixtures of mutually uncorrelated source signals when the sources and mixing parameters are all nonnegative. The uncorrelatedness of the sources is used as a regularization term in the cost function. The main advantage of exploiting uncorrelatedness in this manner is that the inversion of the mixing model, which is a difficult task in the case of determined LQ mixtures, is not required, contrary to the classical LQ methods based on independent component analysis. Experimental results using artificial data and real-world chemical data confirm the effectiveness of our method.