期刊
SIGNAL PROCESSING
卷 92, 期 3, 页码 636-648出版社
ELSEVIER
DOI: 10.1016/j.sigpro.2011.08.023
关键词
Input variable selection; Pattern classification; Feature extraction; Linear discriminant analysis
资金
- NRF
- MEST [400-20100014]
- National Research Foundation of Korea [2009-0078242] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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. (C) 2011 Elsevier B.V. All rights reserved.
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