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Evolving Kernel PCA Pipelines with Evolution Strategies

  • Oliver Kramer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10505)

Abstract

This paper introduces an evolutionary tuning approach for a pipeline of preprocessing methods and kernel principal component analysis (PCA) employing evolution strategies (ES). A simple (1+1)-ES adapts the imputation method, various preprocessing steps like normalization and standardization, and optimizes the parameters of kernel PCA. A small experimental study on a benchmark data set with missing values demonstrates that the evolutionary kernel PCA pipeline can be tuned with relatively few optimization steps, which makes evolutionary tuning applicable to scenarios with very large data sets.

Keywords

Dimension reduction Evolutionary machine learning Machine learning pipelines Kernel PCA 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computational Intelligence Group, Department of Computer ScienceUniversity of OldenburgOldenburgGermany

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