Incomplete multi-view clustering via confidence graph completion based tensor decomposition

In recent years, extensive incomplete multi-view clustering models have been proposed to solve the problem of real-world multi-view data with missing views. However, they still have the following two drawbacks: (1) They ignore the harmful effects of outliers in data and the noise generated during graph recovery. (2) They do not fully explore the high-order relationships among views. To this end, we design a novel graph learning model called confidence graph completion based tensor decomposition (CGCTD) for incomplete multi-view clustering. Specifically, we use the confidence graphs to guide the learning of the complete graphs, which reduces the detrimental effects of outliers and missing samples in the data. Then, we stack the complete graphs of each view into an original tensor to explore high-order relationships and correlations between views. To reduce the negative effects of noise, we decompose the original tensor into an essential tensor and a noise tensor. The essential tensor is introduced to recover the accurate affinity graphs, and the noise tensor aims to model the noise contained in the corrupted graphs. Furthermore, we impose the tensor Schatten p-norm constraint on the essential tensor, which can enhance the low-rank property of the graphs and explore the similarity structure between views. Through extensive experiments on eight benchmark datasets, we demonstrate that the proposed CGCTD outperforms several existing state-of-the-art methods.