In this paper, a noise robust tensor based image clustering approach is proposed which can also perform well in the presence of gross errors. Our major contribution is the improved submodule identification technique in noisy environment by incorporating three important improvements: better low rank representation using reweighted nuclear norm, $$l_\frac{1}{2}$$ regularization to accurately capture sparseness and an error term in the model for noise robustness. Reweighted nuclear norm is introduced in the clustering model to capture self-expressiveness property in a better manner. The $$l_\frac{1}{2}$$ norm regularization is applied in place of $$l_1$$ -norm to properly capture the correlation among data members. An error term is introduced into the model to separate noise and data, which brings a noise robust image clustering technique. Combined effect all the three factors results an accurate clustering method even under the presence of severe noise. The performance of the proposed method is tested on different datasets with varying amount of noise. It is found that the proposed method provides better classification accuracy in almost all conditions as compared with existing methods.

中文翻译：

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基于重加权低秩张量近似和 $$l_{\frac{1}{2}}$$ 正则化的噪声鲁棒图像聚类

在本文中，提出了一种基于噪声鲁棒张量的图像聚类方法，该方法在存在严重错误的情况下也能表现良好。我们的主要贡献是在嘈杂环境中改进了子模块识别技术，通过结合三个重要改进：使用重新加权核范数更好的低秩表示、$$l_\frac{1}{2}$$ 正则化以准确捕获稀疏性和误差项噪声鲁棒性模型。在聚类模型中引入了重新加权的核范数，以更好地捕获自我表达属性。应用 $$l_\frac{1}{2}$$ 范数正则化代替 $$l_1$$ -norm 以正确捕获数据成员之间的相关性。在模型中引入误差项来分离噪声和数据，带来了一种噪声鲁棒的图像聚类技术。即使在存在严重噪声的情况下，所有三个因素的综合作用也会产生准确的聚类方法。所提出方法的性能在具有不同噪声量的不同数据集上进行了测试。发现与现有方法相比，所提出的方法在几乎所有条件下都提供了更好的分类精度。