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Relationship Alignment for View-aware Multi-view Clustering

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
Relationship Alignment; View-Aware Contrastive Learning; Multi-View Clustering
Abstract:

Multi-view clustering improves clustering performance by integrating complementary information from multiple views. However, existing methods often suffer from two limitations: i) the neglect of preserving sample neighborhood structures, which weakens the consistency of inter-sample relationships across views; and ii) inability to adaptively utilize inter-view similarity, resulting in representation conflicts and semantic degradation. To address these issues, we propose a novel framework named Relationship Alignment for View-aware Multi-view Clustering (RAV). Our approach first constructs a sample relationship matrix based on the deep features of each view and aligns it with the global relationship matrix to enhance neighborhood consistency across views and facilitate the accurate measurement of inter-view similarity. Simultaneously, we introduce a view-aware adaptive weighting mechanism for label contrastive learning. This mechanism dynamically adjusts the contrastive intensity between view pairs based on the similarity of their deep features: higher-similarity views lead to stronger label alignment, while lower-similarity views reduce the weighting to prevent forcing inconsistent views into agreement. This strategy effectively promotes cluster-level semantic consistency while preserving natural inter-view relationships. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art approaches on multiple benchmark datasets.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a framework for multi-view clustering that constructs sample relationship matrices from deep features and aligns them with a global relationship matrix to enhance neighborhood consistency. According to the taxonomy, it is positioned in the 'Feature and Prototype Completion Approaches' leaf under 'Incomplete Multi-View Clustering', which contains seven papers including the original work. This leaf addresses missing information recovery through feature imputation and cross-view prediction mechanisms. The presence of six sibling papers suggests a moderately populated research direction within the broader incomplete multi-view clustering domain.

The taxonomy reveals that the paper's leaf sits within a larger branch addressing incomplete data scenarios, distinct from neighboring branches like 'Anchor-Based Incomplete Clustering' (three papers) and 'Distribution-Guided Recovery and Alignment' (three papers). Adjacent major branches include 'View Alignment and Correspondence' (twelve papers across three sub-branches) and 'Consistency and Complementarity Learning' (seven papers). The paper's focus on relationship matrix alignment bridges concepts from its home branch with ideas from 'Cross-View Consistency Alignment', particularly the empty 'Relationship Matrix Alignment' leaf, suggesting it may occupy a relatively underexplored intersection between incomplete data handling and explicit relationship modeling.

Among thirty candidates examined, the contribution-level analysis shows mixed novelty signals. The global-guide-local sample relation alignment module examined ten candidates with zero refutations, suggesting this mechanism may be relatively novel within the limited search scope. The view-aware adaptive weighting mechanism for label contrastive learning examined ten candidates and found one refutable match, indicating some prior work exists in adaptive weighting for contrastive objectives. The overall RAV framework examined ten candidates with zero refutations. These statistics reflect a focused literature search rather than exhaustive coverage, and the single refutation suggests moderate overlap with existing adaptive contrastive learning approaches.

Based on the limited search of thirty semantically similar papers, the work appears to introduce relatively novel mechanisms for relationship alignment in incomplete multi-view clustering, though the adaptive weighting component shows some overlap with prior contrastive learning methods. The taxonomy positioning in a moderately populated leaf and the connection to an empty neighboring leaf ('Relationship Matrix Alignment') suggest the paper may be exploring a less saturated direction within the broader field, though definitive assessment would require more comprehensive literature coverage beyond the top-K semantic matches examined here.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: multi-view clustering with cross-view relationship alignment. The field addresses the challenge of grouping data observed from multiple heterogeneous views while explicitly modeling how instances correspond or relate across these views. The taxonomy reveals several major branches: Incomplete Multi-View Clustering handles scenarios where some views have missing samples or features, often employing feature and prototype completion strategies (e.g., Robust Prototype Completion[16], Cross-View Alignment Completion[38]). View Alignment and Correspondence focuses on establishing correct instance mappings when views are unpaired or only partially aligned (e.g., Fast Unpaired[9], Cross-View Partial Alignment[18]). Consistency and Complementarity Learning seeks to balance shared cluster structure with view-specific information, while Topology and Graph-Based Methods leverage graph representations to capture local and global relationships (e.g., Cross-View Topology[3]). Anchor Learning and Graph Construction emphasizes scalable approaches using representative anchor points (e.g., Automatic Anchor Learning[7], Scalable Anchor Alignment[12]), and Self-Supervised and Contrastive Multi-View Clustering applies contrastive objectives to learn discriminative embeddings. Additional branches cover specialized scenarios such as federated settings, domain-specific applications, and general survey frameworks. A particularly active line of work centers on incomplete and unaligned multi-view data, where methods must simultaneously recover missing information and establish cross-view correspondences under noisy or partial alignment conditions (e.g., Robust Noisy Correspondence[10], Partially View-Aligned[33]). Another contrasting direction emphasizes consistency alignment through graph or prototype matching, trading off computational efficiency for richer relational modeling (e.g., Hierarchical Semantic Alignment[23], Prototype Matching Learning[28]). Relationship Alignment[0] sits within the Feature and Prototype Completion Approaches sub-branch of Incomplete Multi-View Clustering, closely related to works like Robust Prototype Completion[16] and Prototype Matching Learning[28] that also leverage prototype-based representations to handle missing data. Compared to Cross-View Alignment Completion[38], which jointly addresses alignment and completion, Relationship Alignment[0] places stronger emphasis on explicitly modeling cross-view relationships to guide the completion process, reflecting a trend toward integrating alignment mechanisms directly into incomplete clustering frameworks.

Claimed Contributions

Global-guide-local sample relation alignment module

The authors propose a module that constructs sample relationship matrices from deep features for each view and aligns them with a global relationship matrix. This alignment preserves local neighborhood structures while enhancing consistency of inter-sample relationships across multiple views.

10 retrieved papers
View-aware adaptive weighting mechanism for label contrastive learning

The authors introduce an adaptive weighting strategy that dynamically modulates contrastive learning intensity between view pairs based on their deep feature similarity measured by Wasserstein Distance. Higher-similarity views receive stronger alignment weights, while lower-similarity views are assigned smaller weights to prevent forced alignment and representation conflicts.

10 retrieved papers
Can Refute
RA V framework for multi-view clustering

The authors develop an integrated framework that combines cross-view relation alignment with view-aware adaptive label contrastive learning to address limitations in existing multi-view clustering methods, specifically the neglect of neighborhood structure preservation and inability to adaptively utilize inter-view similarity.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Global-guide-local sample relation alignment module

The authors propose a module that constructs sample relationship matrices from deep features for each view and aligns them with a global relationship matrix. This alignment preserves local neighborhood structures while enhancing consistency of inter-sample relationships across multiple views.

Contribution

View-aware adaptive weighting mechanism for label contrastive learning

The authors introduce an adaptive weighting strategy that dynamically modulates contrastive learning intensity between view pairs based on their deep feature similarity measured by Wasserstein Distance. Higher-similarity views receive stronger alignment weights, while lower-similarity views are assigned smaller weights to prevent forced alignment and representation conflicts.

Contribution

RA V framework for multi-view clustering

The authors develop an integrated framework that combines cross-view relation alignment with view-aware adaptive label contrastive learning to address limitations in existing multi-view clustering methods, specifically the neglect of neighborhood structure preservation and inability to adaptively utilize inter-view similarity.

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