From Concrete to Abstract: Multi-View Clustering on Relational Knowledge
Ke Liang, Lingyuan Meng, Hao Li, Jun Wang, Long Lan, Miaomiao Li, Xinwang Liu, Huaimin Wang
Abstract
Multi-view clustering (MVC) is a fast-growing research direction. However, most existing MVC works focus on concrete objects (e.g., cats, desks) but ignore abstract objects (e.g., knowledge, thoughts), which are also important parts of our daily lives and more correlated to cognition. Relational knowledge, as a typical abstract concept, describes the relationship between entities. For example, "Cats like eating fishes," as relational knowledge, reveals the relationship "eating" between "cats" and "fishes." To fill this gap, we first point out that MVC on relational knowledge is considered an important scenario. Then, we construct 8 new datasets to lay research grounds for them. Moreover, a simple yet effective relational knowledge MVC paradigm (RK-MVC) is proposed by compensating the omitted sample-global correlations from the structural knowledge information. Concretely, the basic consensus features are first learned via adopted MVC backbones, and sample-global correlations are generated in both coarse-grained and fine-grained manners. In particular, the sample-global correlation learning module can be easily extended to various MVC backbones. Finally, both basic consensus features and sample-global correlation features are weighted fused as the target consensus feature. We adopt 9 typical MVC backbones in this paper for comparison from 7 aspects, demonstrating the promising capacity of our RK-MVC.