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Multi-view clustering has been empirically shown to improve learning performance by leveraging the inherent complementary information across multiple views of data. However, in real-world scenarios, collecting strictly aligned views is challenging, and learning from both aligned and unaligned data becomes a more practical solution. Partially View-aligned Clustering (PVC) aims to learn correspondences between misaligned view samples to better exploit the potential consistency and complementarity across views, including both aligned and unaligned data. However, most existing PVC methods fail to leverage unaligned data to capture the shared semantics among samples from the same cluster. Moreover, the inherent heterogeneity of multi-view data induces distributional shifts in representations, leading to inaccuracies in establishing meaningful correspondences between cross-view latent features and, consequently, impairing learning effectiveness. To address these challenges, we propose a Semantic MAtching contRasTive learning model (SMART) for PVC. The main idea of our approach is to alleviate the influence of cross-view distributional shifts, thereby facilitating semantic matching contrastive learning to fully exploit semantic relationships in both aligned and unaligned data. Specifically, we mitigate view distribution shifts by aligning cross-view covariance matrices, which enables the inference of a semantic graph for all data. Guided by the learned semantic graph, we further exploit semantic consistency across views through semantic matching contrastive learning. After the optimization of the above mechanisms, our model smoothly performs semantic matching for different view embeddings instead of the cumbersome view realignment, which enables the learned representations to enjoy richer category-level semantics and stronger robustness. Extensive experiments on eight benchmark datasets demonstrate that our method consistently outperforms existing approaches on the PVC problem.

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Unsupervised cell type identification is crucial for uncovering and characterizing heterogeneous populations in single cell omics studies. Although a range of clustering methods have been developed, most focus exclusively on intrinsic cellular structure and ignore the pivotal role of cell-gene associations, which limits their ability to distinguish closely related cell types. To this end, we propose a Refinement Contrastive Learning framework (scRCL) that explicitly incorporates cell-gene interactions to derive more informative representations. Specifically, we introduce two contrastive distribution alignment components that reveal reliable intrinsic cellular structures by effectively exploiting cell-cell structural relationships. Additionally, we develop a refinement module that integrates gene-correlation structure learning to enhance cell embeddings by capturing underlying cell-gene associations. This module strengthens connections between cells and their associated genes, refining the representation learning to exploiting biologically meaningful relationships. Extensive experiments on several single‑cell RNA‑seq and spatial transcriptomics benchmark datasets demonstrate that our method consistently outperforms state-of-the-art baselines in cell-type identification accuracy. Moreover, downstream biological analyses confirm that the recovered cell populations exhibit coherent gene‑expression signatures, further validating the biological relevance of our approach.

@ARTICLE{11268507, author={Peng, Liang and Ye, Yixuan and Liu, Cheng and Che, Hangjun and Wang, Fei and Yu, Zhiwen and Wu, Si and Wong, Hau-San}, journal={IEEE Transactions on Circuits and Systems for Video Technology}, title={SMART: Semantic Matching Contrastive Learning for Partially View-Aligned Clustering}, year={2025}, volume={}, number={}, pages={1-1}, keywords={Muti-View Clustering;Partially View-Aligned Clustering;Contrastive Learning}, doi={10.1109/TCSVT.2025.3636507}}

Recently, neighbor-based contrastive learning has been introduced to effectively exploit neighborhood information for clustering. However, these methods rely on the homophily assumption—that connected nodes share similar class labels and should therefore be close in feature space—which fails to account for the varying homophily levels in real-world graphs. As a result, applying contrastive learning to low-homophily graphs may lead to indistinguishable node representations due to unreliable neighborhood information, making it challenging to identify trustworthy neighborhoods with varying homophily levels in graph clustering. To tackle this, we introduce a novel neighborhood Neutral Contrastive Graph Clustering method NeuCGC that extends traditional contrastive learning by incorporating neutral pairs—node pairs treated as weighted positive pairs, rather than strictly positive or negative. These neutral pairs are dynamically adjusted based on the graph’s homophily level, enabling a more flexible and robust learning process. Leveraging neutral pairs in contrastive learning, our method incorporates two key components: 1) an adaptive contrastive neighborhood distribution alignment that adjusts based on the homophily level of the given attribute graph, ensuring effective alignment of neighborhood distributions, and 2) a contrastive neighborhood node feature consistency learning mechanism that leverages reliable neighborhood information from high-confidence graphs to learn robust node representations, mitigating the adverse effects of varying homophily levels and effectively exploiting highly trustworthy neighborhood information. Experimental results demonstrate the effectiveness and robustness of our approach, outperforming other state-of-the-art graph clustering methods.

Multi-view clustering (MVC) has shown that leveraging both consistency and complementary information across views enhances clustering performance. However, most existing methods focus on aligning features into the same dimension, often neglecting cross-view heterogeneity and introducing discrepancies. To address this, we propose a novel multi-view clustering framework that combines cross-view neighborhood contrastive learning with a cross-attention view-mixup feature learning mechanism. Specifically, the cross-attention view-mixup module learns view-invariant feature representations by capturing complementary and consistent information, while the neighborhood contrastive learning module uncovers semantic structures across views based on the learned mixup features. By implicitly performing feature mixup across views and effectively integrating cross-view neighborhood contrastive learning, our method alleviates cross-view discrepancies and enables more effective integration of complementary and consistent information, ultimately enhancing clustering performance. Experiments conducted on several real datasets demonstrate the effectiveness of our proposed method in comparision with several representative MVC approaches.