Due to the efficiency of exploiting relationships and complex structures hidden in multi-views data, graph-oriented clustering methods have achieved remarkable progress in recent years. But most existing graph-based spectral methods still have the following demerits: (1) They regularize each view equally, which does not make sense in real applications. (2) By employing different norms, most existing methods calculate the error feature by feature, resulting in neglecting the spatial structure information and the complementary information. To tackle the aforementioned drawbacks, we propose an enhanced multi-view spectral clustering model. Our model characterizes the consistency among indicator matrices by minimizing our proposed weighted tensor nuclear norm, which explicitly exploits the salient different information between singular values of the matrix. Moreover, our model adaptively assigns a reasonable weight to each view, which helps improve robustness of the algorithm. Finally, the proposed tensor nuclear norm well exploits both high-order and complementary information, which helps mine the consistency between indicator matrices. Extensive experiments indicate the efficiency of our method.

由于利用多视图数据中隐藏的关系和复杂结构的效率，面向图的聚类方法近年来取得了显着进展。但是大多数现有的基于图的光谱方法仍具有以下缺点：（1）它们均等化每个视图，这在实际应用中是没有意义的。（2）通过采用不同的规范，大多数现有方法逐个特征地计算误差特征，导致忽略了空间结构信息和互补信息。为了解决上述缺点，我们提出了一种增强的多视图光谱聚类模型。我们的模型通过最小化我们建议的加权张量核规范来表征指标矩阵之间的一致性，显式地利用矩阵奇异值之间的显着差异信息。此外，我们的模型为每个视图自适应分配合理的权重，这有助于提高算法的鲁棒性。最后，拟议的张量核规范很好地利用了高阶信息和互补信息，这有助于挖掘指标矩阵之间的一致性。大量的实验表明了我们方法的有效性。