Principal component selection using interactive evolutionary computation

Abstract

We propose a method to solve the selection problem of principal components in machine learning algorithms based on orthogonal transformation using interactive evolutionary computation. The orthogonal transformation presents a linear transformation method that preserves the inner product in the two coordinate systems, the one is before the transformation, and the other is after the transformation. One of the addressed subjects for machine learning algorithms based on orthogonal transformation is how to decide the number of principal components, and which of the principal components should be used to reconstruct the original data. In this work, we use the interactive differential evolution algorithm to study these subjects using real humans’ subjective evaluation in optimization process. An image compression problem using principal component analysis is introduced to study the proposed method. We do not only solve the selection problem of principal components for machine learning algorithms based on orthogonal transformation using interactive evolutionary computation, but also can analyse the human aesthetical characteristics on visual perception and feature selection arising from the designed method and experimental evaluation. We also discuss and analyse potential research subjects and some open topics, which are invited to further investigate.