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Incomplete multi-view clustering is an important technique to deal withreal-world incomplete multi-view data. Previous works assume that all viewshave the same incompleteness, i.e., balanced incompleteness. However, differentviews often have distinct incompleteness, i.e., unbalanced incompleteness,which results in strong views (low-incompleteness views) and weak views(high-incompleteness views). The unbalanced incompleteness prevents us fromdirectly using the previous methods for clustering. In this paper, inspired bythe effective biological evolution theory, we design the novel scheme of viewevolution to cluster strong and weak views. Moreover, we propose an UnbalancedIncomplete Multi-view Clustering method (UIMC), which is the first effectivemethod based on view evolution for unbalanced incomplete multi-view clustering.Compared with previous methods, UIMC has two unique advantages: 1) it proposesweighted multi-view subspace clustering to integrate these unbalancedincomplete views, which effectively solves the unbalanced incomplete multi-viewproblem; 2) it designs the low-rank and robust representation to recover thedata, which diminishes the impact of the incompleteness and noises. Extensiveexperimental results demonstrate that UIMC improves the clustering performanceby up to 40% on three evaluation metrics over other state-of-the-art methods.

Unbalanced Incomplete Multi-View Clustering Via the Scheme of View Evolution: Weak Views are Meat; Strong Views Do Eat