Multi-View Aligned Clustering via Sample-Bundled Optimization: Anchor Graph Enhancement and Contrastive Propagation

Multi-view representation is powerful to capture the complex characteristics of real-world data by integrating complementary information from various modalities. However, in many use cases, such as boiler combustion monitoring, factors including sensor sampling frequency, equipment malfunctions, and network delays can lead to temporal asynchrony in data collection. This asynchrony leads misaligned multi-modal data, furthering the difficulty of learning optimal fused representation. To address this misalignment in multi-view data, a body of methods based on autoencoders and non-negative matrix factorization (NMF) have been presented. However, those methods are incapable of jointly exploring the underlying structure inherent in each view as well as the semantic consistency and structural similarity across views. To these ends, we propose a novel sample-bundled optimization for multi-view aligned clustering, which is based on Enhanced Anchor Graph and Contrastive Propagation (termed asEAGCP). We state that there is semantic consistency among intra-class samples (with the same and cross views) and that the global structures across different views demonstrate similarity. By leveraging these associations, we introduce an enhanced anchor graph with learnable sample correlation and a contrastive graph with feature propagation. Specifically, each anchor graphs preserves the semantic relationship among same-view samples, while the contrastive graph propagates feature information across multi-view samples. Experimental results demonstrate the superiority of our method on benchmark datasets by validating its effectiveness in aligning and clustering multi-view data.