A Comparison on Independent Component Analysis and Principal Component Analysis -for Classification Analysis-
Daehak Kim, Ki-Lak Lee · Journal of the Korean Data and Information Science Society · 2005
We often extract a new feature from the original features for the purpose of reducing the dimensions of feature space and better classification. In this paper, we show feature extraction method based on independent component analysis can be used for classification. Entropy and mutual information are used for the selection of ordered features. Performance of classification based on independent component analysis is compared with principal component analysis for three real data sets.