Abstract
Independent component analysis with reference (ICA-R), is a technique to incorporate prior information about the desired sources as reference signals into the contrast function of ICA so as to form an augmented Lagrangian function under the framework of constrained ICA (cICA). The ICA-R algorithm is constructed by solving the optimization problem via Newton-like learning style. Unfortunately, this algorithm does not find a global optimum once it reaches a local optimum resulting in misconvergence that hinders the capability of ICA-R. To overcome the optimization problems with the previous methods, this paper uses an evolutionary approach to ICA-R that brings the search out of local minima and finds a global optimal solution. Experiments with synthetic signals demonstrate the validity of the proposed method.
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