Sparse-representation-based classification (SRC) has been widely studied and developed for various practical signal classification applications. However, the performance of a SRC-based method is degraded when both the training and test data are corrupted. To counteract this problem, we propose an approach that learns representation with block-diagonal structure (RBDS) for robust image recognition. To be more specific, we first introduce a regularization term that captures the block-diagonal structure of the target representation matrix of the training data. The resulting problem is then solved by an optimizer. Last, based on the learned representation, a simple yet effective linear classifier is used for the classification task. The experimental results obtained on several benchmarking datasets demonstrate the efficacy of the proposed RBDS method. The source code of our proposed RBDS is accessible at https://github.com/yinhefeng/RBDS.

中文翻译：

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学习具有块对角线结构的表示形式以进行模式分类

基于稀疏表示的分类（SRC）已针对各种实际信号分类应用进行了广泛研究和开发。但是，当训练数据和测试数据都损坏时，基于SRC的方法的性能将降低。为了解决这个问题，我们提出了一种学习具有块对角结构（RBDS）的表示方法以实现鲁棒图像识别的方法。更具体地说，我们首先引入一个正则化项，该项捕获训练数据的目标表示矩阵的块对角线结构。然后，由优化程序解决由此产生的问题。最后，基于学习到的表示，将简单而有效的线性分类器用于分类任务。在一些基准数据集上获得的实验结果证明了所提出的RBDS方法的有效性。