Combining independent component analysis with support vector machines

Recently, support vector machine (SVM) has become a popular tool in pattern recognition. In developing a successful SVM classifier, the first step is feature extraction. This paper proposes the application of independent component analysis (ICA) to SVM for feature extraction. In ICA, the original inputs are linearly transformed into features which are mutually statistically independent. By examining the Statlog heart disease data and satimage data, the experimental shows that SVM by feature extraction using ICA can perform better than that without feature extraction