Feature selection based on Kernel pattern similarity

Reduction of feature dimensionality is of considerable importance in machine learning. The generalization performance of classification system improves when correlated and redundant features are removed. In order to reduce the dimensionality of pattern representation, A new feature selection method for Support Vector Machine is proposed. Based on pattern similarity measurement in kernel space, class separability is deduced and we explore the use of the class separability in feature selection. The key idea of our method is that the feature whose removal downgrades the class separability in kernel space most is relevance to the classification. Experiments on linear and nonlinear synthetic problems and real world data sets have been carrierd out to demonstrate the effectiveness of this method.