Outlier Detection with Nonlinear Projection Pursuit
Keywords:outlier detection, nonlinear projections, genetic algorithms
The current work proposes and investigates a new method to identify outliers inÂ multivariate numerical data, driving its roots in projection pursuit. Projection pursuitÂ is basically a method to deliver meaningful linear combinations of attributes. TheÂ novelty of our approach resides in introducing nonlinear combinations, able to modelÂ more complex interactions among attributes. The exponential increase of the searchÂ space with the increase of the polynomial degree is tackled with a genetic algorithmÂ that performs monomial selection. Synthetic test cases highlight the benefits of theÂ new approach over classical linear projection pursuit.
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