Robust multi-view clustering via collaborative constraints and multi-layer concept factorization

The design of effective multi-view clustering algorithms has recently garnered significant research attention. In this paper, we develop a robust multi-view clustering via collaborative constraints and multi-layer concept factorization (RMCCMCF) model to enhance clustering power and robustness by mining low-rank data and hierarchical information from multiple views. This approach adopts low rank (LR) and deep learning (DL) to explore the essential and hierarchical structures hidden in various views. The proposed approach applies multiple graph regularization constraints to extract manifold information from different views. Kernel technology is used for concept factorization in high-dimensional space to effectively distinguish different classes of sample points. Additionally, we design an alternating iterative optimization approach to solve the RMCCMCF algorithm and discuss its convergence. The results of extensive experiments conducted on the CBSR, HW2Sources, 3Sources, CUB, and UCI datasets demonstrate that the RMCCMCF approach outperforms several other recent multi-view clustering approaches in terms of recognition performance.