Principal component selection of machine learning algorithms based on orthogonal transformation by using interactive evolutionary computation

• Published in: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) pp. 000308 - 000313
• Main Author: Yan Pei
• Format: Conference Proceeding
• Language: English; Japanese
• Published: IEEE 01.10.2016
• Subjects: Conferences; Evolutionary computation; IEC; Image coding; image compression; interactive differential evolution; interactive evolutionary computation; Machine learning algorithms; Optimization; orthogonal transformation; Principal component analysis; principal component selection
• DOI: 10.1109/SMC.2016.7844258

We propose a method to solve the selection problem of principal components in machine learning algorithms based on orthogonal transformation by using interactive evolutionary computation. 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 by using real humans' subjective evaluation in an optimization process. An image compression problem using principal component analysis is introduced to study the proposed method. From the evaluation, we do not only solve the selection problem of principal components for machine learning algorithms based on orthogonal transformation, but also can analyse the human aesthetical characteristics on visual perception and feature selection 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.