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Aligning Collaborative View Recovery and Tensorial Subspace Learning via Latent Representation for Incomplete Multi-View Clustering

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Incomplete Multi-view ClusteringCollaborative View RecoveryTensorial Subspace LearningCross-view Correlation Alignment
Abstract:

Multi-view data usually suffer from partially missing views in open scenarios, which inevitably degrades clustering performance. The incomplete multi-view clustering (IMVC) has attracted increasing attention and achieved significant success. Although existing imputation-based IMVC methods perform well, they still face one crucial limitation, i.e., view recovery and subspace representation lack explicit alignment and collaborative interaction in exploring complementarity and consistency across multiple views. To this end, this study proposes a novel IMVC method to Align collaborative view Recovery and tensorial Subspace Learning via latent representation (ARSL-IMVC). Specifically, the ARSL-IMVC infers the complete view from view-shared latent representation and view-specific estimator with Hilbert-Schmidt Independence Criterion regularizer, reshaping the consistent and diverse information intrinsically embedded in original multi-view data. Then, the ARSL-IMVC learns the view-shared and view-specific subspace representations from latent feature and recovered views, and models high-order correlations at the global and local levels in the unified low-rank tensor space. Thus, leveraging the latent representation as a bridge in a unified framework, the ARSL-IMVC seamlessly aligns the complementarity and consistency exploration across view recovery and subspace representation learning, negotiating with each other to promote clustering. Extensive experimental results on seven datasets demonstrate the powerful capacity of ARSL-IMVC in complex incomplete multi-view clustering tasks under various view missing scenarios.

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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.
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Overview

Overall Novelty Assessment

The paper proposes a unified framework that aligns view recovery with tensorial subspace learning through shared latent representations, aiming to capture both consistency and diversity across incomplete views. It resides in the 'Latent Representation Alignment' leaf under 'Matrix Factorization and Subspace Learning', which contains only three papers total (including this one). This leaf represents a relatively focused research direction within the broader 'View Recovery and Imputation Approaches' branch, suggesting the paper targets a specific methodological niche rather than a densely populated area.

The taxonomy reveals that neighboring leaves include 'Direct Matrix Factorization' (four papers) and 'Prototype-Based Recovery' (two papers), both under the same parent node. Adjacent branches explore 'Generative Model-Based Recovery' (six papers across GAN/VAE and diffusion methods) and 'Multiple Imputation' strategies. The paper's emphasis on explicit alignment distinguishes it from direct factorization methods, while its reliance on latent representations rather than generative models or prototypes positions it at the intersection of subspace learning and collaborative imputation, a boundary clarified by the taxonomy's scope notes.

Among 29 candidates examined, the contribution-level analysis indicates substantial prior work overlap. The unified alignment framework (10 candidates examined, 3 refutable) and collaborative recovery mechanism (10 candidates, 2 refutable) both show evidence of overlapping ideas in the limited search scope. The tensorial subspace component (9 candidates, 4 refutable) appears to have the most prior work addressing high-order correlation modeling. These statistics suggest that within the examined literature, each core contribution encounters at least some papers proposing similar alignment, recovery, or tensor-based strategies.

Given the limited search scope of 29 candidates drawn from semantic similarity and citation expansion, the analysis captures a snapshot rather than an exhaustive field survey. The paper's position in a small taxonomy leaf with two siblings suggests it refines an established alignment paradigm rather than opening an entirely new direction. The refutability counts indicate that among the examined candidates, multiple works address overlapping subproblems, though the specific combination of alignment, collaborative recovery, and tensorial modeling may still offer incremental distinctions not fully captured by top-K retrieval.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
29
Contribution Candidate Papers Compared
9
Refutable Paper

Research Landscape Overview

Core task: Incomplete multi-view clustering with missing views addresses the challenge of grouping data when some views are unavailable for certain samples. The field has evolved along several major branches that reflect different philosophies for handling missingness. View Recovery and Imputation Approaches attempt to reconstruct or fill in absent views through matrix factorization, subspace learning, or generative models, enabling downstream clustering on completed data. Imputation-Free Approaches sidestep explicit reconstruction by learning consensus representations directly from available views, often leveraging graph-based or anchor-based methods. Deep Learning Architectures introduce neural encoders and autoencoders to capture complex nonlinear patterns, while Specialized Problem Settings extend the core task to scenarios involving noisy correspondences, federated learning, or unaligned samples. Finally, Optimization and Efficiency Enhancements focus on scalable algorithms and continual learning strategies to handle large-scale or streaming data. Within the View Recovery branch, a particularly active line of work centers on aligning latent representations across views to ensure that imputed features preserve semantic consistency. Collaborative View Recovery[0] exemplifies this direction by jointly learning view-specific embeddings and enforcing alignment constraints, aiming to produce coherent reconstructions even when missingness patterns are severe. This contrasts with earlier methods like Unified Embedding Alignment[50], which directly aligns embeddings without explicit imputation, and Unified Tensor Framework[33], which leverages tensor decomposition to capture higher-order correlations. A key trade-off in this space is between the fidelity of recovered views and the risk of propagating noise or artifacts into the clustering stage. Recent efforts such as High-Confidence Guiding[2] and Bi-Consistency Guidance[5] address this by selectively weighting reliable samples during alignment, while View-Independent Anchors[3] offers a complementary strategy that reduces dependence on view-specific reconstructions altogether. Collaborative View Recovery[0] sits naturally among these latent alignment techniques, emphasizing cross-view collaboration to improve imputation quality while maintaining computational tractability.

Claimed Contributions

Unified framework aligning view recovery and subspace learning via latent representation

The method introduces a unified framework that explicitly aligns collaborative view recovery and tensorial subspace learning through a shared latent representation. This latent representation serves as a bridge to enable coherent cross-view semantic correlation exploration, facilitating interaction between view completion and clustering structure learning.

10 retrieved papers
Can Refute
Collaborative view recovery with consistency-diversity modeling

The method infers complete views from a view-shared latent representation and view-specific estimators with HSIC regularization, reshaping consistent and diverse information across multiple views. This approach ensures reconstruction fidelity while encouraging diversity among recovered views.

10 retrieved papers
Can Refute
Tensorial subspace learning with high-order correlation modeling

The method learns view-shared and view-specific subspace representations from latent features and recovered views, organizing them into a unified low-rank tensor space. This enables modeling of high-order correlations at both global and local levels, facilitating multi-level structural information interaction.

9 retrieved papers
Can Refute

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Unified framework aligning view recovery and subspace learning via latent representation

The method introduces a unified framework that explicitly aligns collaborative view recovery and tensorial subspace learning through a shared latent representation. This latent representation serves as a bridge to enable coherent cross-view semantic correlation exploration, facilitating interaction between view completion and clustering structure learning.

Contribution

Collaborative view recovery with consistency-diversity modeling

The method infers complete views from a view-shared latent representation and view-specific estimators with HSIC regularization, reshaping consistent and diverse information across multiple views. This approach ensures reconstruction fidelity while encouraging diversity among recovered views.

Contribution

Tensorial subspace learning with high-order correlation modeling

The method learns view-shared and view-specific subspace representations from latent features and recovered views, organizing them into a unified low-rank tensor space. This enables modeling of high-order correlations at both global and local levels, facilitating multi-level structural information interaction.

Table of Contents

Aligning Collaborative View Recovery and Tensorial Subspace Learning via Latent Representation for Incomplete Multi-View Clustering | Novelty Validation