Block diagonal representation, which aims to compel the desired representation coefficient to have a block diagonal structure directly, has extensive applications in the domains of computer vision and machine learning. However, single residual modeling in existing works is not robust enough when handling complex noise (i.e., sparse noise and impulsive noise) in reality. To overcome this challenge, a novel Sparseness and Correntropy-based Block Diagonal Representation (SC-BDR) model is proposed, which is able to pursue ideal block diagonal representation and effectively deal with various types of noise. Furthermore, the corresponding optimization algorithm is designed for the proposed problem, and we also conduct extensive experiments to demonstrate the robustness and effectiveness of the SC-BDR model on real-world data.

中文翻译：

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鲁棒子空间聚类的稀疏性和基于熵的块对角线表示

块对角表示旨在迫使所需的表示系数直接具有块对角线结构，在计算机视觉和机器学习领域具有广泛的应用。然而，现有工作中的单一残差建模在处理现实中的复杂噪声（即稀疏噪声和脉冲噪声）时不够鲁棒。为了克服这一挑战，提出了一种新颖的基于稀疏性和相关熵的块对角表示（SC-BDR）模型，该模型能够追求理想的块对角表示并有效处理各种类型的噪声。此外，针对所提出的问题设计了相应的优化算法，并且我们还进行了大量的实验来证明 SC-BDR 模型在真实数据上的鲁棒性和有效性。