判别式
词典学习
计算机科学
稳健性(进化)
人工智能
神经编码
规范(哲学)
理论计算机科学
稀疏逼近
政治学
生物化学
基因
化学
法学
作者
Yulin Sun,Zhao Zhang,Weiming Jiang,Zheng Zhang,Li Zhang,Shuicheng Yan,Meng Wang
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2020-01-14
卷期号:31 (10): 4303-4317
被引量:65
标识
DOI:10.1109/tnnls.2019.2954545
摘要
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 2,1 -norm-based metric to encode the reconstruction error to deliver the robust projective dictionary pairs, and the l 2,1 -norm has the potential to minimize the error. Second, it imposes the robust l 2,1 -norm clearly on the analysis dictionary to ensure the sparse property of the coding coefficients rather than using the costly l 0 /l 1 -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.
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