Dual-Calibration Multi-View Clustering via Compact Anchor Learning

The anchor-based multi-view clustering method has received extensive attention due to its efficiency and scalability in large-scale data scenarios. Existing methods still face significant challenges in optimizing the quality of anchors. Current mainstream approaches typically rely on random sampling strategies or orthogonal constraints for anchor selection and learning. Nevertheless, these methods treat anchor learning and cluster assignment as mutually independent processes handled separately, thereby giving rise to issues including redundant anchor coverage and ambiguous cluster boundaries. Unlike existing anchor-based multi-view clustering methods, this paper innovatively proposes a Dual-Calibration Multi-view Clustering via Compact Anchor Learning (DCMC), which effectively improves anchor quality through a dual-space alignment mechanism. Specifically, DCMC initializes view-specific anchors to capture the underlying data distribution, and then enforces bidirectional consistency between the anchor space and the clustering space to jointly optimize both the sample-to-anchor assignments and the cluster assignments. The alternating optimization process derived from this objective effectively enhances cross-view semantic consistency while preserving the discriminative characteristics of each view. Experimental results demonstrate that DCMC outperforms state-of-the-art methods across multiple benchmark tests, confirming its effectiveness and reliability.