Multiple kernel clustering (MKC) effectively extracts intrinsic and complementary information from data by integrating diverse kernel functions. The allocation of kernel weights is crucial for MKC performance and is closely related to the relationships among kernel matrices. However, it is very difficult to fully capture the intricate relationships among high-dimensional matrices because previous research mostly relies on predefined metrics to characterize the correlation among kernel matrices. To address this challenge, a novel MKC model called AMKC-LRR is proposed that adaptively learns the interrelations among kernel matrices using low-rank representation and unifies this learning process with the clustering task within an optimization framework. Furthermore, an effective alternate optimization algorithm is designed to solve the resulting problem. Extensive experiments and statistical tests conducted on twelve commonly used benchmark datasets show that our proposed model performs favorably in comparison to state-of-the-art MKC methods. The source code for the proposed model is available at https://github.com/bala23-w/AMKC-LRR/.
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