A Study of Feature Extraction and Selection Using Independent Component Analysis

This paper demonstrates some consideration results on feature extraction and selection for handwritten Japanese Hiragana characters using independent component analysis (ICA). In some ICA algorithms where whitening of input signals is introduced as preprocessing, one can consider that the process of feature extraction using ICA consists of two types of transformations: one is the transformation from an input image to its principal components (PCs), and the other is the transformation from PCs to independent components (ICs). From this fact, two types of feature selection can be applied to outputs of these transformations (i.e. PCs and ICs). Furthermore, as criteria of useful features, cumulative proportion can be adopted in the former type of feature selection, and kurtosis can be adopted in the latter. Thus, we present ve different feature selection methods in this paper. To discuss the e ectiveness of these methods, recognition experiments using hand-written Japanese Hiragana characters are carried out. As a result, we show that a hybrid method, in which feature selection is carried out for ICs as well as for PCs, has attractive characteristics if small dimensions of feature vectors are preferred in classi cation.