CORE: Coupled Representation and Cluster-Aware Topology for Scalable Multi-View Clustering

As multi-view data continue to proliferate in real-world applications, anchor-based clustering provides an efficient paradigm for latent cluster discovery. Despite recent progress, existing methods still encounter the following limitations. First, they primarily emphasize multi-view consistency, whereas the learned shared structures lack sufficient cluster discriminability. Second, representation learning and topology optimization are commonly treated as separate processes, restricting mutual refinement during optimization. To address these issues, we propose Coupled Representation and Cluster-Aware Topology (CORE), a scalable multi-view clustering framework designed to leverage cross-view consistency to learn cluster-discriminative structures through unified optimization. Specifically, CORE jointly learns view-specific sample-anchor representations through robust reconstruction and imposes a low-rank tensor structure to capture high-order consistency across views. Meanwhile, an adaptive anchor topology is inferred from representations refined by cross-view consistency and regularized by spectral connectivity constraints to capture cluster-oriented structural information. Accordingly, representation learning and topology optimization are coupled to enable mutual refinement, yielding more discriminative structures for clustering. By performing structural learning in the compact anchor space, CORE avoids expensive sample-wise graph construction while maintaining favorable scalability on large-scale datasets. Extensive experiments on real-world benchmarks demonstrate the effectiveness of CORE across diverse clustering scenarios.