Multi-level Reliable Guidance for Unpaired Multi-view Clustering

• URL: http://arxiv.org/abs/2407.01247v3
• Date: Thu, 16 Oct 2025 12:45:00 GMT
• Title: Multi-level Reliable Guidance for Unpaired Multi-view Clustering
• Authors: Like Xin, Wanqi Yang, Lei Wang, Ming Yang,
• Abstract summary: Unpaired multi-view clustering (UMC) aims to achieve effective joint clustering using unpaired samples observed across multiple views.<n>We propose a novel method, Multi-level Reliable Guidance for UMC (MRG-UMC), which integrates multi-level clustering and reliable view guidance.<n>MRG-UMC outperforms state-of-the-art UMC methods, achieving an average NMI improvement of 12.95% on multi-view datasets.
• Score: 15.600038277342128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this thesis, we address the challenging problem of unpaired multi-view clustering (UMC), which aims to achieve effective joint clustering using unpaired samples observed across multiple views. Traditional incomplete multi-view clustering (IMC) methods typically rely on paired samples to capture complementary information between views. However, such strategies become impractical in the UMC due to the absence of paired samples. Although some researchers have attempted to address this issue by preserving consistent cluster structures across views, effectively mining such consistency remains challenging when the cluster structures {with low confidence}. Therefore, we propose a novel method, Multi-level Reliable Guidance for UMC (MRG-UMC), which integrates multi-level clustering and reliable view guidance to learn consistent and confident cluster structures from three perspectives. Specifically, inner-view multi-level clustering exploits high-confidence sample pairs across different levels to reduce the impact of boundary samples, resulting in more confident cluster structures. Synthesized-view alignment leverages a synthesized-view to mitigate cross-view discrepancies and promote consistency. Cross-view guidance employs a reliable view guidance strategy to enhance the clustering confidence of poorly clustered views. These three modules are jointly optimized across multiple levels to achieve consistent and confident cluster structures. Furthermore, theoretical analyses verify the effectiveness of MRG-UMC in enhancing clustering confidence. Extensive experimental results show that MRG-UMC outperforms state-of-the-art UMC methods, achieving an average NMI improvement of 12.95\% on multi-view datasets. {The source code is available at: https://anonymous.4open.science/r/MRG-UMC-5E20.

Related papers

• Deep Incomplete Multi-View Clustering via Hierarchical Imputation and Alignment [15.396375506151102]
  We propose a novel deep IMVC framework that integrates hierarchical imputation and alignment with four key components.<n> Experiments on benchmarks demonstrate that our framework achieves superior performance under varying levels of missingness.
  arXiv  Detail & Related papers  (2026-01-14T00:46:00Z)
• 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)
• Adversarial Fair Multi-View Clustering [7.650076926241037]
  We propose an adversarial fair multi-view clustering (AFMVC) framework that integrates fairness learning into the representation learning process.<n>Our framework achieves superior fairness and competitive clustering performance compared to existing multi-view clustering and fairness-aware clustering methods.
  arXiv  Detail & Related papers  (2025-08-06T04:07:08Z)
• 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)
• Deep Incomplete Multi-view Clustering with Distribution Dual-Consistency Recovery Guidance [69.58609684008964]
  We propose BURG, a novel method for incomplete multi-view clustering with distriBution dUal-consistency Recovery Guidance. We treat each sample as a distinct category and perform cross-view distribution transfer to predict the distribution space of missing views. To compensate for the lack of reliable category information, we design a dual-consistency guided recovery strategy that includes intra-view alignment guided by neighbor-aware consistency and cross-view alignment guided by prototypical consistency.
  arXiv  Detail & Related papers  (2025-03-14T02:27:45Z)
• Structure-guided Deep Multi-View Clustering [13.593229506936682]
  Deep multi-view clustering seeks to utilize the abundant information from multiple views to improve clustering performance. Most of the existing clustering methods often neglect to fully mine multi-view structural information. We propose a structure-guided deep multi-view clustering model to explore the distribution of multi-view data.
  arXiv  Detail & Related papers  (2025-01-17T12:42:30Z)
• Unpaired Multi-view Clustering via Reliable View Guidance [7.441454668534061]
  This paper focuses on unpaired multi-view clustering (UMC), a challenging problem where paired observed samples are unavailable across multiple views. We propose Reliable view Guidance with one reliable view (RG-UMC) and multiple reliable views (RGs-UMC) for UMC. Both RG-UMC and RGs-UMC outperform the best state-of-the-art method by an average of 24.14% and 29.42% in NMI, respectively.
  arXiv  Detail & Related papers  (2024-04-27T13:03:57Z)
• CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network [53.72046586512026]
  We propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net) It captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework. Based on the human cognition, i.e., learning from easy to hard, it introduces a self-paced strategy to select the most confident samples for model training.
  arXiv  Detail & Related papers  (2024-03-28T15:45:03Z)
• Efficient Multi-View Graph Clustering with Local and Global Structure Preservation [59.49018175496533]
  We propose a novel anchor-based multi-view graph clustering framework termed Efficient Multi-View Graph Clustering with Local and Global Structure Preservation (EMVGC-LG) Specifically, EMVGC-LG jointly optimize anchor construction and graph learning to enhance the clustering quality. In addition, EMVGC-LG inherits the linear complexity of existing AMVGC methods respecting the sample number.
  arXiv  Detail & Related papers  (2023-08-31T12:12:30Z)
• DealMVC: Dual Contrastive Calibration for Multi-view Clustering [78.54355167448614]
  We propose a novel Dual contrastive calibration network for Multi-View Clustering (DealMVC) We first design a fusion mechanism to obtain a global cross-view feature. Then, a global contrastive calibration loss is proposed by aligning the view feature similarity graph and the high-confidence pseudo-label graph. During the training procedure, the interacted cross-view feature is jointly optimized at both local and global levels.
  arXiv  Detail & Related papers  (2023-08-17T14:14:28Z)
• Multi-View Clustering via Semi-non-negative Tensor Factorization [120.87318230985653]
  We develop a novel multi-view clustering based on semi-non-negative tensor factorization (Semi-NTF) Our model directly considers the between-view relationship and exploits the between-view complementary information. In addition, we provide an optimization algorithm for the proposed method and prove mathematically that the algorithm always converges to the stationary KKT point.
  arXiv  Detail & Related papers  (2023-03-29T14:54:19Z)
• MCoCo: Multi-level Consistency Collaborative Multi-view Clustering [15.743056561394612]
  Multi-view clustering can explore consistent information from different views to guide clustering. We propose a novel Multi-level Consistency Collaborative learning framework (MCoCo) for multi-view clustering.
  arXiv  Detail & Related papers  (2023-02-26T16:08:53Z)
• Adaptively-weighted Integral Space for Fast Multiview Clustering [54.177846260063966]
  We propose an Adaptively-weighted Integral Space for Fast Multiview Clustering (AIMC) with nearly linear complexity. Specifically, view generation models are designed to reconstruct the view observations from the latent integral space. Experiments conducted on several realworld datasets confirm the superiority of the proposed AIMC method.
  arXiv  Detail & Related papers  (2022-08-25T05:47:39Z)
• A Survey on Incomplete Multi-view Clustering [66.50475816827208]
  In practical applications, such as disease diagnosis, multimedia analysis, and recommendation system, not all views of samples are available in many cases. Incomplete multi-view clustering is referred to as incomplete multi-view clustering.
  arXiv  Detail & Related papers  (2022-08-17T03:00:59Z)
• Deep Multi-View Semi-Supervised Clustering with Sample Pairwise Constraints [10.226754903113164]
  We propose a novel Deep Multi-view Semi-supervised Clustering (DMSC) method, which jointly optimize three kinds of losses during networks finetuning. We demonstrate that our proposed approach performs better than the state-of-the-art multi-view and single-view competitors.
  arXiv  Detail & Related papers  (2022-06-10T08:51:56Z)
• Locality Relationship Constrained Multi-view Clustering Framework [5.586948325488168]
  Locality Relationship Constrained Multi-view Clustering Framework (LRC-MCF) is presented. It aims to explore the diversity, geometric, consensus and complementary information among different views. LRC-MCF takes sufficient consideration to weights of different views in finding the common-view locality structure.
  arXiv  Detail & Related papers  (2021-07-11T15:45:10Z)
• You Never Cluster Alone [150.94921340034688]
  We extend the mainstream contrastive learning paradigm to a cluster-level scheme, where all the data subjected to the same cluster contribute to a unified representation. We define a set of categorical variables as clustering assignment confidence, which links the instance-level learning track with the cluster-level one. By reparametrizing the assignment variables, TCC is trained end-to-end, requiring no alternating steps.
  arXiv  Detail & Related papers  (2021-06-03T14:59:59Z)
• Unsupervised Multi-view Clustering by Squeezing Hybrid Knowledge from Cross View and Each View [68.88732535086338]
  This paper proposes a new multi-view clustering method, low-rank subspace multi-view clustering based on adaptive graph regularization. Experimental results for five widely used multi-view benchmarks show that our proposed algorithm surpasses other state-of-the-art methods by a clear margin.
  arXiv  Detail & Related papers  (2020-08-23T08:25:06Z)

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.