Multiobjective projection pursuit for semisupervised feature extraction

Abstract

The current paper presents a framework for linear feature extraction applicable in both unsupervised and supervised data analysis, as well as in their hybrid - the semi-supervised scenario. New features are extracted in a filter manner with a multi-modal genetic algorithm that optimizes simultaneously several projection indices. Experimental results show that the new algorithm is able to provide a compact and improved representation of the data set. The use of mixed labeled and unlabeled data under this scenario improves considerably the performance of constrained clustering algorithms such as constrained k-Means. © Springer-Verlag Berlin Heidelberg 2013.