Paper Info
Title
Discriminative Local Sparse Representation by Robust Adaptive Dictionary Pair Learning
Abstract
In this article, we propose a structured robust adaptive dictionary pair learning (RA-DPL) framework for the discriminative sparse representation (SR) learning. To achieve powerful representation ability of the available samples, the setting of RA-DPL seamlessly integrates the robust projective DPL, locality-adaptive SRs, and discriminative coding coefficients learning into a unified learning framework. Specifically, RA-DPL improves existing projective DPL in four perspectives. First, it applies a sparse l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm-based metric to encode the reconstruction error to deliver the robust projective dictionary pairs, and the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm has the potential to minimize the error. Second, it imposes the robust l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm clearly on the analysis dictionary to ensure the sparse property of the coding coefficients rather than using the costly l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> /l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm. As such, the robustness of the data representation and the efficiency of the learning process are jointly considered to guarantee the efficacy of our RA-DPL. Third, RA-DPL conceives a structured reconstruction weight learning paradigm to preserve the local structures of the coding coefficients within each class clearly in an adaptive manner, which encourages to produce the locality preserving representations. Fourth, it also considers improving the discriminating ability of coding coefficients and dictionary by incorporating a discriminating function, which can ensure high intraclass compactness and interclass separation in the code space. Extensive experiments show that our RA-DPL can obtain superior performance over other state of the arts.
Year
DOI
Venue
2020
10.1109/TNNLS.2019.2954545
IEEE Transactions on Neural Networks and Learning Systems
Keywords
DocType
Volume
Image recognition,image representation,locality-adaptive discriminative sparse representation (SR),robust projective dictionary pair learning (DPL)
Journal
31
Issue
ISSN
Citations 
10
2162-237X
8
PageRank 
References 
Authors
0.42
28
7
Name
Order
Citations
PageRank
Yulin Sun1121.81
Zhao Zhang293865.99
Weiming Jiang31008.50
Zheng Zhang454940.45
Li Zhang536339.03
Shuicheng Yan680.42
Meng Wang73094167.38