http://hdl.handle.net/10453/283 Sat, 18 May 2013 15:40:47 GMT 2013-05-18T15:40:47Z http://hdl.handle.net/10453/8108 A Hybrid Nonlinear-Discriminant Analysis Feature Projection Technique Nguyen Hung; Khushaba Rami N; Al-Jumaily Adel; Al-Ani Ahmed Wobcke, Wayne; Zhang, Mengjie 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. Tue, 01 Jan 2008 00:00:00 GMT http://hdl.handle.net/10453/8108 2008-01-01T00:00:00Z