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Efficient Fisher Discrimination Dictionary Learning

机译：高效Fisher歧视词典学习

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摘要

Fisher Determination Dictionary Learning (FDDL) has shown to be effective in image classification. However, the Original FDDL (O-FDDL) method is time-consuming. To address this issue, a fast Simplified FDDL (S-FDDL) method was proposed. But S-FDDL ignores the role of collaborative reconstruction, thus having an unstable performance in classification tasks with unbalanced changes in different classes. This paper focuses on developing an Efficient FDDL (E-FDDL) method, which is more suitable for such classification problems. Precisely, instead of solving the original Fisher Discrimination based Sparse Representation (FDSR) problem, we propose to solve an Approximate FDSR (A-FDSR) problem whose objective function is an upper bound of that of FDSR. A-FDSR considers the role of both the discriminative reconstruction and the collaborative reconstruction. This makes E-FDDL stable when dealing with classification tasks with unbalanced changes in different classes. Furthermore, fast optimization strategies are applicable to A-FDSR, thus leading to the high efficiency of E-FDDL which can be explained by analysis on convergence rate and computational complexity. We also use E-FDDL to accelerate the Shared Domain-adapted Dictionary Learning (SDDL) algorithm which is a FDDL based new method for domain adaptation. Experimental results on face and object recognition demonstrate the stable and fast performance of E-FDDL.
机译：费舍尔确定字典学习（FDDL）已显示在图像分类中有效。但是，原始FDDL（O-FDDL）方法非常耗时。为了解决此问题，提出了一种快速的简化FDDL（S-FDDL）方法。但是S-FDDL忽略了协作重建的作用，因此在分类任务中表现不稳定，并且不同类别的变化不平衡。本文着重于开发一种更有效的FDDL（E-FDDL）方法，该方法更适用于此类分类问题。确切地说，不是解决原始的基于Fisher歧视的稀疏表示（FDSR）问题，而是提出解决目标函数为FDSR上限的近似FDSR（A-FDSR）问题。 A-FDSR同时考虑了歧视性重建和协作性重建的作用。这使得E-FDDL在处理具有不同类别中不平衡变化的分类任务时很稳定。此外，快速优化策略适用于A-FDSR，从而导致E-FDDL的高效率，这可以通过分析收敛速度和计算复杂性来解释。我们还使用E-FDDL来加速共享域自适应词典学习（SDDL）算法，该算法是基于FDDL的域自适应新方法。人脸和物体识别的实验结果证明了E-FDDL的稳定和快速性能。

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  • 来源
    《Signal processing》 |2016年第11期|28-39|共12页
  • 作者

    Rui Jiang; Hong Qiao; Bo Zhang;

  • 作者单位

    State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;

    State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China,CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences, Shanghai 200031, China;

    LSEC and Institute of Applied Mathematics, AMSS, Chinese Academy of Sciences, Beijing 100190, China;

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  • 正文语种 eng
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  • 关键词

    Fisher discrimination dictionary learning; Nesterov's accelerated gradient method; Face recognition; Domain adaptation;

    机译：费舍尔歧视字典学习;内斯特罗夫的加速梯度法;人脸识别;领域适应;

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