Unbalanced Incomplete Multi-View Clustering Via the Scheme of View Evolution: Weak Views are Meat; Strong Views Do Eat
Xiang Fang, Yuchong Hu, Pan Zhou, Dapeng Oliver Wu · IEEE Transactions on Emerging Topics in Computational Intelligence · 2021
Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous methods assume that all views have the same incompleteness, i.e., balanced incompleteness. However, different views often have distinct incompleteness, i.e., unbalanced incompleteness, which results instrong views(low-incompleteness views) andweak views(high-incompleteness views). The unbalanced incompleteness prevents us from directly using previous methods. In this paper, inspired by the effective biological evolution theory, we design the novel scheme ofview evolutionto cluster strong and weak views. Moreover, we propose an Unbalanced Incomplete Multi-view Clustering method (UIMC), which is thefirsteffective method based on view evolution for unbalanced incomplete multi-view clustering. Compared with previous methods, UIMC has two unique advantages: 1) it proposes weighted multi-view subspace clustering to integrate unbalanced incomplete views, which effectively solves the unbalanced incomplete multi-view clustering problem; 2) it designs the low-rank representation to recover the data, which diminishes the impact of the incompleteness and noises. Extensive experimental results demonstrate that UIMC improves the clustering performance by up to 40% on three evaluation metrics over other state-of-the-art methods. We provide codes for all of our experiments inhttps://github.com/ZeusDavide/TETCI_UIMC.