Nonparametric density estimation based independent component analysis via particle swarm optimization

The paper investigates the application of a modified particle swarm optimization technique to nonparametric density estimation based independent component analysis (ICA). Nonparametric ICA has the advantage over traditional ICA techniques in that its performance is not dependent upon prior assumptions about the source distributions. Particle swarm optimization (PSO) is similar to the genetic algorithm in that it utilizes a population based search suitable for optimizing multimodal error surfaces where gradient-based algorithms tend to fail, such as those generated by nonlinear entropy maximization schemes used in ICA algorithms.