Abstract
Kernel mapping has attracted a great deal of attention from researchers in the field of pattern recognition and statistical machine learning. Kernel-based approaches are the best choice whenever a non-linear classification model is needed. This paper proposes a nonlinear classification approach based on the non-parametric version of Fisher’s discriminant analysis. This technique can efficiently find a nonpara-metric kernel representation where linear discriminants perform better. Data classification is achieved by integrating the linear version of the nonparametric Fisher’s discriminant analysis with the kernel map-ping. Based on the kernel trick, we provide a new formulation for Fisher’s criterion, defined solely in terms of the internal dot-product of the original input data. The obtained experimental results have dem- onstrated the competitiveness of our approach compared to major state of the art approaches.