Unbalanced incomplete multi-view clustering based on low-rank tensor graph learning.

Incomplete multi-view clustering (IMVC) methods have attracted extensive attention in the field of clustering due to their superior performance in addressing incomplete multi-view data. However, existing IMVC methods often address balanced incomplete multi-view data, i.e., the missing rate of each view is the same, which does not match reality. In real life, the missing rate of each view in incomplete multi-view data is often different; these are referred to as unbalanced incomplete multi-view data. However, few articles consider the processing of unbalanced incomplete multi-view data. Therefore, we propose an innovative method, unbalanced incomplete multi-view clustering based on low-rank tensor graph learning (UIMVC/LTGL), to handle unbalanced incomplete multi-view data. Specifically, we first use the adjacency relationship between views to adaptively complete similarity graph matrices. To explore the consistency and high-order correlation among views, we further introduce a consensus representation learning term and low-rank tensor constraint into UIMVC/LTGL. In practical applications, each view's contribution to clustering should be different, especially for UIMVC problems. Therefore, we also apply the adaptive weight strategy to each view, which makes reasonable use of the information of each view. The abovementioned steps are integrated into a unified framework to obtain the optimal clustering effect. The augmented Lagrange multiplier (ALM) method is employed to solve the optimization problem. The experimental results on seven well-known datasets fully demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]