Nonlinear mapping using particle swarm optimisation

Large datasets consisting of high-dimensional vectors commonly describe complex objects. Having these vectors exist in a smaller dimension where the topological characteristics of the original space are preserved, allows clusters or patterns inherent in the data to be identified. This paper investigates the capability of various particle swarm optimisation (PSO) structures to effectively map a high-dimensional dataset to a lower-dimensional set. Four different local nonlinear mapping methods are investigated. Results obtained from the experiments give a clear indication of which nonlinear method to use when certain conditions hold