Particle Swarm Optimization in Exploratory Data Analysis

摘要

We discuss extensions of particle swarm based optimization (PSO) algorithms in the context of exploratory data analysis. In particular, we apply these extensions to principal component analysis, exploratory projection pursuit and topology preserving mappings. Our extensions include combining PSO algorithms with stochastic sampling and a form of reinforcement learning known as Q-learning. We illustrate on a variety of artificial data sets and show that our new results are better than previous results on such data sets.