Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning

• URL: http://arxiv.org/abs/2505.11182v1
• Date: Fri, 16 May 2025 12:37:10 GMT
• Title: Imputation-free and Alignment-free: Incomplete Multi-view Clustering Driven by Consensus Semantic Learning
• Authors: Yuzhuo Dai, Jiaqi Jin, Zhibin Dong, Siwei Wang, Xinwang Liu, En Zhu, Xihong Yang, Xinbiao Gan, Yu Feng,
• Abstract summary: 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.
• Score: 65.75756724642932
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In incomplete multi-view clustering (IMVC), missing data induce prototype shifts within views and semantic inconsistencies across views. A feasible solution is to explore cross-view consistency in paired complete observations, further imputing and aligning the similarity relationships inherently shared across views. Nevertheless, existing methods are constrained by two-tiered limitations: (1) Neither instance- nor cluster-level consistency learning construct a semantic space shared across views to learn consensus semantics. The former enforces cross-view instances alignment, and wrongly regards unpaired observations with semantic consistency as negative pairs; the latter focuses on cross-view cluster counterparts while coarsely handling fine-grained intra-cluster relationships within views. (2) Excessive reliance on consistency results in unreliable imputation and alignment without incorporating view-specific cluster information. Thus, we propose an IMVC framework, imputation- and alignment-free for consensus semantics learning (FreeCSL). To bridge semantic gaps across all observations, we learn consensus prototypes from available data to discover a shared space, where semantically similar observations are pulled closer for consensus semantics learning. To capture semantic relationships within specific views, we design a heuristic graph clustering based on modularity to recover cluster structure with intra-cluster compactness and inter-cluster separation for cluster semantics enhancement. Extensive experiments demonstrate, compared to state-of-the-art competitors, FreeCSL achieves more confident and robust assignments on IMVC task.

Related papers

• 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)
• Multi-view Granular-ball Contrastive Clustering [15.732090918798395]
  Granular balls lie between instances and clusters, naturally preserving the local topological structure of the sample set.<n>We propose a method named Multi-view Granular-ball Contrastive Clustering (MGBCC)
  arXiv  Detail & Related papers  (2024-12-18T06:53:30Z)
• 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)
• Deep Contrastive Multi-view Clustering under Semantic Feature Guidance [8.055452424643562]
  We propose a multi-view clustering framework named Deep Contrastive Multi-view Clustering under Semantic feature guidance (DCMCS) By minimizing instance-level contrastive loss weighted by semantic similarity, DCMCS adaptively weakens contrastive leaning between false negative pairs. Experimental results on several public datasets demonstrate the proposed framework outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-03-09T02:33:38Z)
• Efficient Bilateral Cross-Modality Cluster Matching for Unsupervised Visible-Infrared Person ReID [56.573905143954015]
  We propose a novel bilateral cluster matching-based learning framework to reduce the modality gap by matching cross-modality clusters. Under such a supervisory signal, a Modality-Specific and Modality-Agnostic (MSMA) contrastive learning framework is proposed to align features jointly at a cluster-level. Experiments on the public SYSU-MM01 and RegDB datasets demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2023-05-22T03:27:46Z)
• 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)
• 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)
• Deep Clustering by Semantic Contrastive Learning [67.28140787010447]
  We introduce a novel variant called Semantic Contrastive Learning (SCL) It explores the characteristics of both conventional contrastive learning and deep clustering. It can amplify the strengths of contrastive learning and deep clustering in a unified approach.
  arXiv  Detail & Related papers  (2021-03-03T20:20:48Z)
• Agglomerative Neural Networks for Multi-view Clustering [109.55325971050154]
  We propose the agglomerative analysis to approximate the optimal consensus view. We present Agglomerative Neural Network (ANN) based on Constrained Laplacian Rank to cluster multi-view data directly. Our evaluations against several state-of-the-art multi-view clustering approaches on four popular datasets show the promising view-consensus analysis ability of ANN.
  arXiv  Detail & Related papers  (2020-05-12T05:39:10Z)

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.