Input variable selection for feature extraction in classification problems

Authors:
Sang-Il Choi;Jiyong Oh;Chong-Ho Choi;Chunghoon Kim
Affiliations:
Department of Applied Computer Engineering, Dankook University, 126, Jukjeon-dong, Suji-gu, Yongin-si, Gyeonggi-do 448-701, South Korea;School of Electrical Engineering and Computer Science, Seoul National University, #047, San 56-1, Sillim-dong, Gwanak-gu, Seoul 151-744, South Korea;School of Electrical Engineering and Computer Science, Seoul National University, #047, San 56-1, Sillim-dong, Gwanak-gu, Seoul 151-744, South Korea;Qualcomm Korea R&D Center, Seoul 443-749, South Korea
Venue:
Signal Processing
Year:
2012

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Abstract

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.