http://hdl.handle.net/10453/283 2013-05-24T10:13:17Z 2013-05-24T10:13:17Z Nguyen Hung Khushaba Rami N Al-Jumaily Adel Al-Ani Ahmed http://hdl.handle.net/10453/8108 2012-05-03T05:24:36Z 2008-01-01T00:00:00Z

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.

2008-01-01T00:00:00Z