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SRSC: Selective, Robust, and Supervised Constrained Feature Representation for Image Classification.

Authors :
Xie, Guo-Sen
Zhang, Zheng
Liu, Li
Zhu, Fan
Zhang, Xu-Yao
Shao, Ling
Li, Xuelong
Source :
IEEE Transactions on Neural Networks & Learning Systems; Oct2020, Vol. 31 Issue 10, p4290-4302, 13p
Publication Year :
2020

Abstract

Feature representation learning, an emerging topic in recent years, has achieved great progress. Powerful learned features can lead to excellent classification accuracy. In this article, a selective and robust feature representation framework with a supervised constraint (SRSC) is presented. SRSC seeks a selective, robust, and discriminative subspace by transforming the original feature space into the category space. Particularly, we add a selective constraint to the transformation matrix (or classifier parameter) that can select discriminative dimensions of the input samples. Moreover, a supervised regularization is tailored to further enhance the discriminability of the subspace. To relax the hard zero-one label matrix in the category space, an additional error term is also incorporated into the framework, which can lead to a more robust transformation matrix. SRSC is formulated as a constrained least square learning (feature transforming) problem. For the SRSC problem, an inexact augmented Lagrange multiplier method (ALM) is utilized to solve it. Extensive experiments on several benchmark data sets adequately demonstrate the effectiveness and superiority of the proposed method. The proposed SRSC approach has achieved better performances than the compared counterpart methods. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2162237X
Volume :
31
Issue :
10
Database :
Complementary Index
Journal :
IEEE Transactions on Neural Networks & Learning Systems
Publication Type :
Periodical
Accession number :
146358977
Full Text :
https://doi.org/10.1109/TNNLS.2019.2953675