A method based on L-BFGS to solve constrained complex-valued ICA - Nguyen, Anh Hai Trieu (2020)
Complex-valued independent component analysis (ICA) is a celebrated method in blind separation of complex-valued signals. In this paper, we propose to transform the constrained optimization problems of complex-valued ICA into unconstrained optimization problems which can be solved by limited-memory Broyden–Fletcher–Goldfarb–Shanno update (L-BFGS). As opposed to previous approaches, the proposed method does not apply any restriction on the Hessian matrix of ICA cost function. It can separate mixed sub-Gaussian, super-Gaussian, circular, and non-circular sources. Simulations show promising results.