期刊
DIGITAL SIGNAL PROCESSING
卷 55, 期 -, 页码 78-84出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.dsp.2016.05.001
关键词
Feature extraction; Face recognition; LRC; Discriminant analysis; Adaptive
资金
- National Natural Science Foundation of China [61503195, 61502245]
- NUPTSF [NY214165]
- Natural Science Fund for Colleges and Universities in Jiangsu Province [15KJB520026]
- China Postdoctoral Science Foundation [2015M571786]
- Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology) [30916014107]
- Natural Science Foundation of Jiangsu Province [BK20150849]
Linear discriminant regression classification (LDRC) was presented recently in order to boost the effectiveness of linear regression classification (LRC). LDRC aims to find a subspace for LRC where LRC can achieve a high discrimination for classification. As a discriminant analysis algorithm, however, LDRC considers an equal importance of each training sample and ignores the different contributions of these samples to learn the discriminative feature subspace for classification. Motivated by the fact that some training samples are more effectual in learning the low-dimensional feature space than other samples, in this paper, we propose an adaptive linear discriminant regression classification (ALDRC) algorithm by taking special consideration of different contributions of the training samples. Specifically, ALDRC makes use of different weights to characterize the different contributions of the training samples and utilizes such weighting information to calculate the between-class and the within-class reconstruction errors, and then ALDRC seeks to find an optimal projection matrix that can maximize the ratio of the between-class reconstruction error over the within-class reconstruction error. Extensive experiments carried out on the AR, FERET and ORL face databases demonstrate the effectiveness of the proposed method. (C) 2016 Elsevier Inc. All rights reserved.
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