Input variable selection for feature extraction in classification problems

被引：23

作者：

Choi, Sang-Il ^{[1
]}

Oh, Jiyong ^{[2
]}

Choi, Chong-Ho ^{[2
]}

Kim, Chunghoon ^{[3
]}

机构：

[1] Dankook Univ, Dept Appl Comp Engn, Yongin 448701, Gyeonggi Do, South Korea

[2] Seoul Natl Univ, Sch Elect Engn & Comp Sci, Seoul 151744, South Korea

[3] Qualcomm Korea R&D Ctr, Seoul 443749, South Korea

来源：

SIGNAL PROCESSING | 2012年 / 92卷 / 03期

关键词：

Input variable selection; Pattern classification; Feature extraction; Linear discriminant analysis; FACE; RECOGNITION; PATTERN; DISCRIMINANT;

D O I：

10.1016/j.sigpro.2011.08.023

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：636 / 648

页数：13