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Debiased and Denoised Projection Learning for Incomplete Multi-view Clustering

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Incomplete multi-view clusteringprojection debiasing and denoisingrobust contrastive learning.
Abstract:

Multi-view clustering achieves outstanding performance but relies on the assumption of complete multi-view samples. However, certain views may be partially unavailable due to failures during acquisition or storage, resulting in distribution shifts across views. Although some incomplete multi-view clustering (IMVC) methods have been proposed, they still confront the following limitations: 1) Missing-view data imputation methods increase the unnecessary computational complexity; 2) Consensus representation imputation methods always ignore the inter-view distribution bias due to missing views. To tackle these issues, we propose a novel IMVC based on projection debiasing and denoising (PDD). Specifically, it utilizes the unbiased projection learned from complete views to refine the biased projection learned from data with missing views. Additionally, we introduce a robust contrastive learning for consensus projection to mitigate cluster collapse risk induced by misalignment noise. Comprehensive experiments demonstrate that PDD achieves superior performance compared with state-of-the-art methods.

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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 projection debiasing and denoising framework for incomplete multi-view clustering, avoiding explicit missing-view imputation. It resides in the 'Contrastive Learning for Consensus' leaf under 'Consensus Representation Learning', which contains five papers total. This leaf sits within a broader taxonomy of fifty papers spanning nine major branches, indicating a moderately populated research direction. The focus on refining biased projections and mitigating cluster collapse through robust contrastive learning positions the work within an active but not overcrowded subfield.

The taxonomy reveals that consensus representation learning is one of several parallel strategies for handling incomplete multi-view data. Neighboring branches include 'Data Completion and Imputation Strategies' (e.g., diffusion-based completion, tensor reconstruction) and 'Imputation-Free and Direct Clustering Approaches' (e.g., anchor-based scalable methods, weighted fusion). The paper's sibling works in the same leaf explore contrastive or adversarial frameworks for consensus, while adjacent leaves address mutual information maximization and deep embedding fusion. This structural context suggests the paper builds on established contrastive paradigms while diverging from imputation-heavy or graph-centric methods.

Among twenty-one candidates examined, the contribution-level analysis shows mixed novelty signals. The debiased projection learning strategy examined one candidate and found one refutable overlap, suggesting prior work addresses similar refinement ideas. The robust contrastive learning contribution examined ten candidates with one refutable match, indicating some overlap but also substantial unexplored space among the nine non-refutable candidates. The overall DDP-IMVC framework examined ten candidates with no refutations, suggesting the integrated approach may offer incremental novelty. These statistics reflect a limited search scope, not exhaustive coverage.

Given the top-twenty-one semantic matches analyzed, the work appears to offer moderate novelty within its leaf. The debiased projection and robust contrastive components show partial overlap with existing methods, while the combined framework may represent a fresh integration. The taxonomy structure confirms this is an active research direction with established contrastive baselines, but the limited search scope means broader field coverage remains uncertain. Further examination of imputation-free and graph-based branches could reveal additional relevant prior work.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
21
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: incomplete multi-view clustering with missing data. The field addresses scenarios where multiple feature representations (views) of the same data are available, but some views may be entirely absent for certain samples or contain missing entries. The taxonomy reveals several major branches: Data Completion and Imputation Strategies focus on reconstructing missing views before clustering, while Imputation-Free and Direct Clustering Approaches bypass explicit imputation and learn cluster structures directly from incomplete observations. Consensus Representation Learning aims to extract a unified latent representation that integrates information across views, often leveraging contrastive or adversarial techniques. Graph-Based and Similarity Learning Methods construct affinity graphs or similarity matrices to capture cross-view relationships, and Matrix Factorization and Kernel-Based Methods decompose data into low-rank or kernel-induced structures. Additional branches cover Specialized Incomplete Data Scenarios (e.g., federated or unpaired settings), Online and Continual Learning Settings for streaming data, and Theoretical and Robustness Frameworks that provide guarantees or handle noisy observations. Representative works such as COMPLETER[8] and early foundations like Incomplete multi-view clustering[18] illustrate the evolution from basic imputation to more sophisticated deep learning and tensor-based solutions. Recent activity has concentrated on consensus representation learning and contrastive methods, where the goal is to align view-specific embeddings into a shared semantic space despite missing data. Debiased and Denoised Projection[0] falls within the Contrastive Learning for Consensus cluster, emphasizing the removal of noise and bias in learned projections to improve clustering quality. This contrasts with purely imputation-driven approaches like COMPLETER[8], which explicitly reconstructs missing views, and with scalable graph-based methods such as Scalable incomplete multi-view clustering[3], which prioritize computational efficiency over deep representation learning. Nearby works like Incomplete contrastive multi-view clustering[2] and Adversarial incomplete multi-view clustering[32] also explore contrastive or adversarial frameworks, but Debiased and Denoised Projection[0] distinguishes itself by targeting projection quality and robustness. Open questions remain around balancing imputation fidelity, computational cost, and the ability to generalize across diverse missingness patterns, with ongoing exploration of tensor decompositions, self-supervised signals, and continual learning paradigms.

Claimed Contributions

Debiased projection learning via unbiased refinement strategy

The authors introduce an attention-based refinement mechanism that uses unbiased projections from complete samples to correct distribution shifts in biased projections from incomplete samples, thereby constructing robust consensus projections in a common embedding space.

1 retrieved paper
Can Refute
Robust contrastive learning for consensus projection

The authors propose a denoised contrastive learning strategy that balances between MAE loss and InfoNCE loss through truncation, preventing cluster collapse caused by misalignment noise when completing missing views with consensus projections.

10 retrieved papers
Can Refute
DDP-IMVC framework for incomplete multi-view clustering

The authors develop a complete framework that combines adaptive projection matrices based on cluster separability, unbiased refinement for correcting distribution shifts, and robust contrastive constraints to handle incomplete multi-view clustering without explicit data imputation.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Debiased projection learning via unbiased refinement strategy

The authors introduce an attention-based refinement mechanism that uses unbiased projections from complete samples to correct distribution shifts in biased projections from incomplete samples, thereby constructing robust consensus projections in a common embedding space.

Contribution

Robust contrastive learning for consensus projection

The authors propose a denoised contrastive learning strategy that balances between MAE loss and InfoNCE loss through truncation, preventing cluster collapse caused by misalignment noise when completing missing views with consensus projections.

Contribution

DDP-IMVC framework for incomplete multi-view clustering

The authors develop a complete framework that combines adaptive projection matrices based on cluster separability, unbiased refinement for correcting distribution shifts, and robust contrastive constraints to handle incomplete multi-view clustering without explicit data imputation.

Table of Contents