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.
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Breaban, M.E. (2013). Multiobjective Projection Pursuit for Semisupervised Feature Extraction. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_33
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DOI: https://doi.org/10.1007/978-3-642-37192-9_33
Publisher Name: Springer, Berlin, Heidelberg
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