Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67411
Title: Discriminative elastic-net regularized linear regression
Authors: Zhang, Z
Lai, ZH 
Xu, Y
Shao, L
Wu, J
Xie, GS
Keywords: Elastic-net regularization
Discriminative methods
Linear regression
Image classification
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on image processing, 2017, v. 26, no. 3, p. 1466-1481 How to cite?
Journal: IEEE transactions on image processing 
Abstract: In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zero-one matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of these methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability. To this end, we present an elastic-net regularized linear regression (ENLR) framework, and develop two robust linear regression models which possess the following special characteristics. First, our methods exploit two particular strategies to enlarge the margins of different classes by relaxing the strict binary targets into a more feasible variable matrix. Second, a robust elastic-net regularization of singular values is introduced to enhance the compactness and effectiveness of the learned projection matrix. Third, the resulting optimization problem of ENLR has a closed-form solution in each iteration, which can be solved efficiently. Finally, rather than directly exploiting the projection matrix for recognition, our methods employ the transformed features as the new discriminate representations to make final image classification. Compared with the traditional linear regression model and some of its variants, our method is much more accurate in image classification. Extensive experiments conducted on publicly available data sets well demonstrate that the proposed framework can outperform the state-of-the-art methods. The MATLAB codes of our methods can be available at http://www.yongxu.org/lunwen.html.
URI: http://hdl.handle.net/10397/67411
ISSN: 1057-7149
DOI: 10.1109/TIP.2017.2651396
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

1
Citations as of Aug 18, 2017

WEB OF SCIENCETM
Citations

1
Last Week
1
Last month
Citations as of Aug 20, 2017

Page view(s)

24
Last Week
5
Last month
Checked on Aug 20, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.