SMART: Semantic Matching Contrastive Learning for Partially View-Aligned Clustering

• URL: http://arxiv.org/abs/2512.15396v1
• Date: Wed, 17 Dec 2025 12:48:41 GMT
• Title: SMART: Semantic Matching Contrastive Learning for Partially View-Aligned Clustering
• Authors: Liang Peng, Yixuan Ye, Cheng Liu, Hangjun Che, Fei Wang, Zhiwen Yu, Si Wu, Hau-San Wong,
• Abstract summary: Partially View-aligned Clustering aims to learn correspondences between misaligned view samples.<n>Our approach is to alleviate the influence of cross-view distributional shifts, thereby facilitating semantic matching contrastive learning.<n>Our method consistently outperforms existing approaches on the PVC problem.
• Score: 46.33455475152849
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 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. Extensive experiments on eight benchmark datasets demonstrate that our method consistently outperforms existing approaches on the PVC problem.

Related papers

• Global-Graph Guided and Local-Graph Weighted Contrastive Learning for Unified Clustering on Incomplete and Noise Multi-View Data [17.546021510992908]
  Real-world multi-view data suffer from data incompleteness or noise, resulting in rare-paired samples or mis-paired samples.<n>We propose a unified CL-based MVC framework for enhancing clustering effectiveness on incomplete and noise multi-view data.<n>Our method is imputation-free and can be integrated into a unified global-local graph-guided contrastive learning framework.
  arXiv  Detail & Related papers  (2025-12-25T05:41:05Z)
• Generalized Deep Multi-view Clustering via Causal Learning with Partially Aligned Cross-view Correspondence [72.41989962665285]
  Multi-view clustering (MVC) aims to explore the common clustering structure across multiple views.<n>However, real-world scenarios often present a challenge as only partial data is consistently aligned across different views.<n>We design a causal multi-view clustering network, termed CauMVC, to tackle this problem.
  arXiv  Detail & Related papers  (2025-09-19T14:31:40Z)
• Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning [65.75756724642932]
  In incomplete multi-view clustering, missing data induce prototype shifts within views and semantic inconsistencies across views.<n>We propose an IMVC framework, imputation- and alignment-free for consensus semantics learning (FreeCSL)<n>FreeCSL achieves more confident and robust assignments on IMVC task, compared to state-of-the-art competitors.
  arXiv  Detail & Related papers  (2025-05-16T12:37:10Z)
• Incomplete Multi-view Clustering via Diffusion Contrastive Generation [10.303281347345955]
  We propose a novel IMVC method called Diffusion Contrastive Generation (DCG)<n>DCG learns the distribution characteristics to enhance clustering by applying forward diffusion and reverse denoising processes to intra-view data.<n>It integrates instance-level and category-level interactive learning to exploit the consistent and complementary information available in multi-view data.
  arXiv  Detail & Related papers  (2025-03-12T09:27:25Z)
• Partial Multi-View Clustering via Meta-Learning and Contrastive Feature Alignment [13.511433241138702]
  Partial multi-view clustering (PVC) presents significant challenges practical research problem for data analysis in real-world applications. Existing clustering methods struggle to handle incomplete views effectively, leading to suboptimal clustering performance. We propose a novel dual optimization framework based on contrastive learning, which aims to maximize the consistency of latent features in incomplete multi-view data.
  arXiv  Detail & Related papers  (2024-11-14T19:16:01Z)
• Discriminative Anchor Learning for Efficient Multi-view Clustering [59.11406089896875]
  We propose discriminative anchor learning for multi-view clustering (DALMC) We learn discriminative view-specific feature representations according to the original dataset. We build anchors from different views based on these representations, which increase the quality of the shared anchor graph.
  arXiv  Detail & Related papers  (2024-09-25T13:11:17Z)
• Hierarchical Mutual Information Analysis: Towards Multi-view Clustering in The Wild [9.380271109354474]
  This work proposes a deep MVC framework where data recovery and alignment are fused in a hierarchically consistent way to maximize the mutual information among different views. To the best of our knowledge, this could be the first successful attempt to handle the missing and unaligned data problem separately with different learning paradigms.
  arXiv  Detail & Related papers  (2023-10-28T06:43:57Z)
• Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment [50.82982601256481]
  We propose a Cross-view Partial Sample and Prototype Alignment Network (CPSPAN) for Deep Incomplete Multi-view Clustering. Unlike existing contrastive-based methods, we adopt pair-observed data alignment as 'proxy supervised signals' to guide instance-to-instance correspondence construction.
  arXiv  Detail & Related papers  (2023-03-28T02:31:57Z)
• Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data [52.491074276133325]
  Multi-view representation learning has developed rapidly over the past decades and has been applied in many fields. We propose a new cross-view graph contrastive learning framework, which integrates multi-view information to align data and learn latent representations. Experiments conducted on several real datasets demonstrate the effectiveness of the proposed method on the clustering and classification tasks.
  arXiv  Detail & Related papers  (2022-11-08T09:19:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.