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Neural Computing and Applications
Published in: Neural Computing and Applications 10/2019

28-02-2018 | Original Article

Robust collaborative representation-based classification via regularization of truncated total least squares

Authors: Shaoning Zeng, Bob Zhang, Yuandong Lan, Jianping Gou

Published in: Neural Computing and Applications | Issue 10/2019

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Abstract

Collaborative representation-based classification has shown promising results on cognitive vision tasks like face recognition. It solves a linear problem with \(l_1\) or \(l_2\) norm regularization to obtain a stable sparse representation. Previous studies showed that the collaboration representation assisted the output of optimum sparsity constraint, but the choice of regularization also played a crucial role in stable representation. In this paper, we proposed a novel discriminative collaborative representation-based classification method via regularization implemented by truncated total least squares algorithm. The key idea of the proposed method is combining two coefficients obtained by \(l_2\) regularization and truncated TLS-based regularization. After evaluated by extensive experiments conducted on several benchmark facial databases, the proposed method is demonstrated to outperform the naive collaborative representation-based method, as well as some other state-of-the-art methods for face recognition. The regularization by truncation effectively and dramatically enhances sparsity constraint on coding coefficients in collaborative representation and increases robustness for face recognition.
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Metadata
Title
Robust collaborative representation-based classification via regularization of truncated total least squares
Authors
Shaoning Zeng
Bob Zhang
Yuandong Lan
Jianping Gou
Publication date
28-02-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 10/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3403-7

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