Incomplete Multiview Clustering via Low-Rank Tensor Ring Completion.

Since real-world multiview data frequently contains numerous samples that are not observed from some viewpoints, the incomplete multiview clustering (IMC) issue has received a great deal of attention recently. However, most existing IMC methods choose to zero-fill the missing instances, which leads to the failure to exploit information hidden in the missing instances, and high-order interactions between various views. To tackle these problems, we proposed an effective IMC method using low-rank tensor ring completion, which was demonstrated to be powerful in exploiting high-order correlation. Specifically, we first stack the incomplete similarity graphs of all views into a 3(rd)-order incomplete tensor and then restore it via the tensor ring decomposition. Next, using an adaptive weighting technique, we apply multiview spectral clustering to all entire graphs in order to balance the contributions of different viewpoints and identify the consensus representation for grouping. Finally, we employ the alternating direction method of multipliers (ADMM) to optimize the suggested model. Numerous experimental findings on numerous different datasets show that the suggested approach is superior to other cutting-edge approaches.