Improved support vector classification using PCA and ICA feature space modification

An approach that unifies subspace feature selection and optimal classification is presented. Independent component analysis (ICA) and principal component analysis (PCA) provide a maximally variant or statistically independent basis for pattern recognition. A support vector classifier (SVC) provides information about the significance of each feature vector. The feature vectors and the principal and independent component bases are modified to …