Input variable selection for feature extraction in classification problems

We propose an input variable selection method based on discriminant features. By analyzing the relationship between the input space and feature space obtained by discriminant analysis, the input variables that contain a large amount of discriminative information are selected, while input variables with less discriminative information are discarded. By this, the signal to noise ratio of the data can be improved. The proposed method can be applied not only to the feature extraction methods based on covariance matrix but also to the methods based on image covariance matrix. The experimental results obtained with various data sets show that the proposed method results in improved classification performance regardless of the dimension and type of data.

► The proposed method selects input variables based on a discriminant analysis.
► The proposed method can be simply applied to any type of feature extraction method.
► It can improve the performance of feature extraction by eliminating the noisy variables.