A Hybrid Nonlinear-Discriminant Analysis Feature Projection Technique

Authors:
Rami N. Khushaba;Ahmed Al-Ani;Adel Al-Jumaily;Hung T. Nguyen
Affiliations:
Faculty of Engineering, University of Technology, Sydney, Sydney, Australia Broadway 2007;Faculty of Engineering, University of Technology, Sydney, Sydney, Australia Broadway 2007;Faculty of Engineering, University of Technology, Sydney, Sydney, Australia Broadway 2007;Faculty of Engineering, University of Technology, Sydney, Sydney, Australia Broadway 2007
Venue:
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

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Abstract

Feature set dimensionality reduction via Discriminant Analysis (DA) is one of the most sought after approaches in many applications. In this paper, a novel nonlinear DA technique is presented based on a hybrid of Artificial Neural Networks (ANN) and the Uncorrelated Linear Discriminant Analysis (ULDA). Although dimensionality reduction via ULDA can present a set of statistically uncorrelated features, but similar to the existing DA's it assumes that the original data set is linearly separable, which is not the case with most real world problems. In order to overcome this problem, a one layer feed-forward ANN trained with a Differential Evolution (DE) optimization technique is combined with ULDA to implement a nonlinear feature projection technique. This combination acts as nonlinear discriminant analysis. The proposed approach is validated on a Brain Computer Interface (BCI) problem and compared with other techniques.