Title: SparseMVC: Probing Cross-view Sparsity Variations for Multi-view Clustering
Venue: NeurIPS 2025
Accepted: yes
Keywords: Muti-view clustering

self-supervised learning

representation learning

autoencoder
Abstract: Existing multi-view clustering methods employ various strategies to address data-level sparsity and view-level dynamic fusion. However, we identify a critical yet overlooked issue: varying sparsity across views.  Cross-view sparsity variations lead to encoding discrepancies, heightening sample-level semantic heterogeneity and making view-level dynamic weighting inappropriate. To tackle these challenges, we propose Adaptive Sparse Autoencoders for Multi-View Clustering (SparseMVC), a framework with three key modules. Initially, the sparse autoencoder probes the sparsity of each view and adaptively adjusts encoding formats via an entropy-matching loss term, mitigating cross-view inconsistencies. Subsequently, the correlation-informed sample reweighting module employs attention mechanisms to assign weights by capturing correlations between early-fused global and view-specific features, reducing encoding discrepancies and balancing contributions. Furthermore, the cross-view distribution alignment module aligns feature distributions during the late fusion stage, accommodating datasets with an arbitrary number of views. Extensive experiments demonstrate that SparseMVC achieves state-of-the-art clustering performance. Our framework advances the field by extending sparsity handling from the data-level to view-level and mitigating the adverse effects of encoding discrepancies through sample-level dynamic weighting. The source code is publicly available at https://github.com/cleste-pome/SparseMVC.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis manuscript explicitly introduces and analyzes the phenomenon of cross-view sparsity variations in multi-view data, a challenge that has been largely overlooked by existing clustering methods. In response to this newly identified issue, the authors propose SparseMVC as a targeted and systematic solution. SparseMVC employs an adaptive sparse autoencoder that dynamically adjusts encoding constraints for each view according to its sparsity level, reweights sample contributions based on correlations between local and global features to mitigate inconsistencies, and aligns feature distributions across views to enhance clustering robustness and thereby improve generalization.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: The manuscript introduces a novel, data-driven approach to addressing cross-view sparsity variations, a challenge that has been largely overlooked in prior research. By focusing on sparsity at the view level, the authors offer a refined and effective solution, SparseMVC, which enhances both the accuracy and robustness of clustering results. SparseMVC is meticulously designed, incorporating three key components that collectively tackle the problem from multiple angles. Each module is distinct yet seamlessly integrated, ensuring a comprehensive approach to the complexities posed by varying sparsity across views. Furthermore, the paper is exceptionally clear and well-structured, beginning with a concise problem statement and thoroughly explaining the rationale behind the proposed solution, making the complex ideas both accessible and academically rigorous. Lastly, the significance of this work lies in its emphasis on addressing inherent data characteristics, rather than merely refining existing models. By shifting the focus toward data-driven model design, this paper opens up new avenues for future research in multi-view learning.\n\nWeaknesses: While the method demonstrates strong performance across a variety of datasets, its effectiveness in real-world applications with noisy or incomplete data remains to be fully explored. Further analysis is needed to evaluate how well SparseMVC handles extreme cases of sparsity or data imperfections in practical scenarios. Additionally, while the model shows strong performance in the downstream clustering task, its applicability to other tasks remains unexplored. The figures, especially the diagrams illustrating the modules, could be improved, particularly in terms of the consistency of symbols and the definitions of certain input variables.\n\nQUESTIONS:\n(1) How does the correlation-informed sample reweighting module differ from attention-based or other dynamic weighting methods in multi-view clustering? What limitations of these prior approaches does it address?\n(2) Why does the SparseMVC framework apply an additional transformation to the encoded latent features (Z), producing compressed representations (Y)? What motivates this design choice?\n(3) What criteria guided the selection of the sparsity threshold (τ)?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper rigorously quantifies and identifies variations in view-level sparsity ratios across a wide range of multi-view data, presenting an innovative adaptive, unsupervised representation learning network. While prior research on sparsity in multi-view clustering has predominantly focused on addressing sparsity at the dataset level, this work makes a significant contribution by targeting internal view sparsity discrepancies within a dataset. Specifically, it introduces SparseMVC, a framework built upon a sparse autoencoder with adaptive constraints, further enhanced by a correlation-informed sample reweighting module designed to alleviate the impact of encoding disparities and constraint-induced inconsistencies.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- Quality: This paper demonstrates a commendable level of quality, with a methodologically sound and well-executed design that reflects careful engineering and a solid understanding of the multi-view clustering problem. The experimental design is rigorous, and the empirical results are convincingly presented, showing clear improvements over existing baselines.\nClarity: In terms of clarity, the paper is written with precision and coherence. The problem is articulated clearly from the outset, and the architectural design is logically motivated and systematically explained. The visualizations are informative and enhance the reader's understanding of the proposed framework.\n\n- Significance: Regarding significance, the paper tackles a meaningful yet underexplored issue in the field—the cross-view sparsity problem—which is both practically relevant and theoretically challenging. By directing attention toward data-level issues rather than solely on architectural refinements, the work offers a compelling shift in perspective that could inspire future research directions.\n\n- Originality: With respect to originality, the proposed SparseMVC framework exhibits an impressive level of conceptual novelty. The integration of adaptive sparsity modeling, sample reweighting based on correlation, and distribution alignment constitutes a multifaceted and coherent strategy that differs from conventional multi-view learning paradigms. Each module contributes distinctly to the overarching objective, and their unified deployment reflects thoughtful innovation.\n\nWeaknesses: \n\n- Although the framework is reasonably efficient, the use of contrastive learning inherently imposes computational overhead, making it unlikely to rank among the fastest available approaches. \n\n- The assumption of well-aligned views may limit its robustness in practical scenarios where slight inter-view misalignments are often unavoidable.\n\nQUESTIONS:\n- CDA module is designed to align feature distributions across views. Could you provide a more detailed technical explanation of how this alignment is accomplished? Are there any potential risks of overfitting when this alignment is applied too aggressively across views?\n\n- Could the correlation computation between global and view-specific features in the CSR module be compromised by noise introduced from highly sparse views? How is the reliability of the resulting attention weights ensured under such conditions?\n\n- Could you provide a more detailed explanation for the choice of Sv in Eq. (3)?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-06-24"}

comments: SUMMARY:
This paper proposes a multi-view clustering method focusing on the sparsity difference across views. Thanks to the design of the sparse autoencoder, the correlation-informed sample reweighting module, and the cross-view distribution alignment module, the proposed method achieves the state-of-the-art performance on eight datasets.

STRENGTHS AND WEAKNESSES:
- Strengths
	1. The proposed method is evaluated on eight datasets and achieves promising clustering performance.
- Weaknesses
	1. The motivation of this work is questionable. Why must sparsity correspond to less informative features? The information about zeros depends on its physical meaning behind. For example, true zero values also represent the characteristics of the sample. 
	2. While this work claims that sparsity harms the multi-view clustering performance, the method seems to be designed for handling low-quality features in general. In other words, only a few components in the proposed method are specifically designed to handle data sparsity. In fact, the most related design, sparse auto-encoder, has already been proposed in previous works. As a result, the contribution of this work seems limited.
	3. The datasets used for evaluations are too small. Methods evaluated and tuned on datasets with only hundreds of points might not generalize well to other datasets and scenarios.
	4. The proposed method is also sensitive to hyperparameters, which are not easy to tune in practice, given the unsupervised nature of clustering.

QUESTIONS:
I would like the authors to address my concerns raised in the weaknesses section.

LIMITATIONS:
Yes.
TITLE: Official Review
CLARITY: 2
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-06-25

comments: SUMMARY:
This paper addresses the varying sparsity problem in multi-view data by proposing an adaptive sparse autoencoder for MVC. To mitigate encoding disparities across views, an attention mechanism is incorporated based on inter-view correlations. Furthermore, to enhance representation consistency, a contrastive loss is introduced to align view-specific representations with a unified global representation.

STRENGTHS AND WEAKNESSES:
Strength:
This paper tackles cross-view sparsity variation in multi-view learning and conducts experiments on datasets with pronounced sparsity differences across views, such as ALOI-100.
Weakness:
1.	The method addresses sparsity variation by applying an adaptive weight based on an existing entropy-matching loss. This solution is relatively simplistic; additional theoretical justification is recommended to support the claimed contribution.
2.	Most of the evaluated datasets are small-scale, typically containing only a few hundred samples, which may limit the robustness of the conclusions.
3.	The method lacks comparisons with MVC approaches specifically designed to handle data-level sparsity. An ablation study of the adaptive weight component is also necessary to validate the effectiveness of the proposed SSA. As currently presented, the results do not convincingly argue the method’s ability to handle varying sparsity.

QUESTIONS:
1. How is multi-view data fused in the early fusion strategy?
2. Why does the sparsity of the ALOI-100 dataset differ between Figure 1 and Table 1? There appears to be an inconsistency in the reported sparsity of the ALOI-100 dataset between Figure 1 and Table 1.

LIMITATIONS:
YES
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-27