Consensus-Guided Deep Multi-View Clustering with Dual Representation Learning

Multi-view clustering aims to leverage complementary information from different data sources to improve clustering performance. However, many existing methods emphasize cross-view consensus in deep representations while underexploiting structural cues retained in shallower features, leaving complementary information underused and making the learned clusters less discriminative. Moreover, they often explore clustering patterns through centroid assignment or alignment, insufficiently modeling inter-sample relationships. Same-cluster samples may remain dispersed while different-cluster samples lie close together, leaving clusters dispersed with unclear boundaries. To address these challenges, we propose COnsensus-guided deep multi-view clustering with dual REpresentation learning (CORE), a framework that combines multi-hierarchical representation learning, semantic invariance, and hard-samples contrastive alignment. Specifically, CORE employs deep and shallow branches to extract abstract semantics and preserve structural cues, respectively, allowing the model to use complementary information when learning discriminative representations. It then constructs a global clustering target from concatenated deep representations to guide semantic invariance across views. Our analysis derives an explicit bound showing when a discriminative view’s distance advantage outweighs conflicts to determine the consensus alignment. Finally, CORE uses consensus-derived pseudo-labels to jointly mine hard positive and negative samples. Contrastive learning then increases the similarity of the selected hard positives and decreases that of the hard negatives, improving separation between clusters. Extensive experiments demonstrate that our method significantly outperforms existing state-of-the-art methods.