Outlier Detection with Nonlinear Projection Pursuit

Abstract

The current work proposes and investigates a new method to identify outliers in multivariate numerical data, driving its roots in projection pursuit. Projection pursuit is basically a method to deliver meaningful linear combinations of attributes. The novelty of our approach resides in introducing nonlinear combinations, able to model more complex interactions among attributes. The exponential increase of the search space with the increase of the polynomial degree is tackled with a genetic algorithm that performs monomial selection. Synthetic test cases highlight the benefits of the new approach over classical linear projection pursuit.