Title: Scalable Cross-View Sample Alignment for Multi-View Clustering with View Structure Similarity
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multi-view Clustering; K-means Clustering; Similarity Graph Learning; Representation Learning
Abstract: Most existing multi-view clustering methods aim to generate a consensus partition across all views, based on the assumption that all views share the same sample arrangement. However, in real-world scenarios, the collected data across different views is often unsynchronized, making it difficult to ensure consistent sample correspondence between views. To address this issue, we propose a scalable sample-alignment-based multi-view clustering method, referred to as SSA-MVC. Specifically, we first employ a cluster-label matching (CLM) algorithm to select the view whose clustering labels best match those of the others as the benchmark view. Then, for each of the remaining views, we construct representations of non-aligned samples by computing their similarities with aligned samples. Based on these representations, we build a similarity graph between the non-aligned samples of each view and those in the benchmark view, which serves as the alignment criterion. This alignment criterion is then integrated into a late-fusion framework to enable clustering without requiring aligned samples. Notably, the learned sample alignment matrix can be used to enhance existing multi-view clustering methods in scenarios where sample correspondence is unavailable. The effectiveness of the proposed SSA-MVC algorithm is validated through extensive experiments conducted on eight real-world multi-view datasets.


Peer reviews:
comments: SUMMARY:
A scalable multi-view clustering algorithm based on sample alignment is proposed in this paper, where an alignment matrix is learned and incorporated into a late-fusion framework to facilitate clustering in the absence of aligned samples.

STRENGTHS AND WEAKNESSES:
a. The manuscript is well-written and introduces a novel perspective on addressing sample misalignment in multi-view clustering.

b. Experimental results on eight benchmark datasets demonstrate the effectiveness of the proposed method.

c. It remains unclear whether cross-view similarity between unaligned representations can reliably capture semantic correspondence under strong view heterogeneity.

d. The scalability of the method to large-scale data is not thoroughly discussed, particularly regarding the computational implications and potential optimizations.

e. While the use of a k-nearest neighbor graph is intuitive, the sensitivity of the model to the choice of k warrants further analysis.

QUESTIONS:
a. Without explicit correspondence, what principle underlies the computation of cross-view similarity between non-aligned samples?

b. What is the rationale for enforcing orthogonality on the alignment matrix M? A clear justification is currently lacking and should be provided.

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 4
DATE: 2025-06-25

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nA novel multi-view clustering method based on cross-view sample alignment is proposed in this manuscript, and its effectiveness is demonstrated through experiments on eight multi-view datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:  \n(1) The paper addresses the challenge of multi-view clustering with unaligned samples in a principled and practical manner. The proposed framework is conceptually clear and yields competitive results across multiple benchmarks.  \n(2) The use of cluster-label matching (CLM) for benchmark view selection is a noteworthy design choice. This mechanism appears to improve alignment stability, particularly in the presence of inconsistent or noisy views.  \n(3) The introduction of an alignment graph to connect aligned and unaligned samples enhances representational flexibility and contributes to performance gains under partial correspondence settings.  \n\nPoints for Consideration:  \n(1) It would be helpful for the authors to clarify the specific motivations behind adopting the CLM mechanism for benchmark view selection. A brief discussion comparing it to alternative strategies (e.g., view consensus or uncertainty-based methods) could further strengthen the justification.  \n(2) As the alignment process is anchored on a single benchmark view, there is a potential risk that misrepresentative or noisy data within this view could influence the quality of alignment across other views. Some discussion on the mitigation of such risks would be valuable.  \n(3) Constructing similarity graphs between non-aligned and aligned samples is central to the proposed method. However, the paper could benefit from further analysis on how initial clustering inaccuracies might affect the propagation of alignment errors.  \n(4) While the alignment matrix plays a key role in sample correspondence, its robustness under noisy benchmark views remains unclear. Additional empirical or theoretical insight into the stability of alignment in such cases would enhance the completeness of the work.\n\nQUESTIONS:\nWhile the proposed late-fusion strategy elegantly avoids the need for early alignment, it raises an interesting question: to what extent does this design truly exploit the structural and semantic complementarities between views? One might wonder whether certain types of cross-view information—particularly those that only emerge through joint modeling—are inadvertently underutilized in the alignment process.\n\nAnother intriguing aspect is the reliance on cluster-label matching for selecting a benchmark view. This appears to work well in practice, yet it remains somewhat unclear how robust this step is to imperfections in the initial clustering. Given that early-stage clustering often involves noise or instability, especially in high-dimensional or heterogeneous views, it would be worth exploring how such variance might influence the view selection outcome—and, by extension, the final alignment quality.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe proposed method addresses unalignment samples in multi-view clustering by aligning views through a benchmark-based cluster-label matching strategy and constructing cross-view similarity graphs, enabling effective clustering without requiring sample correspondence.\n\nSTRENGTHS AND WEAKNESSES:\nThe strengths of this manuscript are listed as follows:\n\nThe proposed method effectively handles unalignment multi-view data by eliminating the need for strict sample correspondence across views; The use of cluster-label matching to select a benchmark view ensures more reliable and consistent alignment across different views; By integrating the alignment criterion into a late-fusion framework, SSA-MVC maintains scalability and flexibility across diverse datasets.\n\nThe weaknesses of this manuscript are listed as follows:\n\nCan the proposed method be effectively applied to cases where samples are entirely unaligned across views; the same approach is used for both unaligned sample representation and cross-view similarity—are these the only feasible choices, or can alternative methods be employed; as alignment performance depends on the benchmark view, how is its reliability ensured; while the datasets are standard in multi-view clustering, could the authors clarify how they were preprocessed to simulate sample misalignment?\n\nQUESTIONS:\n1. Can the proposed method be effectively applied to cases where samples are entirely unaligned across views?\n\n2. The same approach is used for both unaligned sample representation and cross-view similarity—are these the only feasible choices, or can alternative methods be employed?\n\n3. As alignment performance depends on the benchmark view, how is its reliability ensured?\n\n4. While the datasets are standard in multi-view clustering, could the authors clarify how they were preprocessed to simulate sample misalignment?\n\n5. The proposed method belongs to MVC methods with shallow models, so it is recommended to compare more SOTA shallow MVC methods like tensor-based or subspace-based methods.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-28"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a scalable sample-alignment-based multi-view clustering method. The proposed approach first employs a cluster-label matching algorithm to select the view, and then constructs representations of non-aligned samples by computing their similarities with aligned samples. Extensive experiments demonstrate the effectiveness of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n(1) This paper proposes a novel alignment strategy to address the issue with partially aligned views. Extensive experiments demonstrate the effectiveness of the proposed method.\n\nWeaknesses:\n(1) The title of this paper is 'Scalable Cross-View Sample Alignment for Multi-View Clustering with View Structure Similarity'. However, it lacks the most important experiments with fully misaligned views. This raises a question: Is the proposed method unable to address the issue of fully misaligned views?\n\n(2) This paper considers the issue of view misalignment. Therefore, performance validation should be compared more with the SOTA methods of view misalignment instead of traditional multi-view clustering methods.\n\n(3) The proposed method uses a feature rotation matrix to align the feature space. Is the rotation matrix effective for the sample, i.e., can the learned rotation matrix be used to output aligned samples?\n\n(4) The writing of this paper can be further improved. For example, the roles of each component in Eqs. (4), (5) and (7) should be specifically explained.\n\nQUESTIONS:\n(1) The title of this paper is 'Scalable Cross-View Sample Alignment for Multi-View Clustering with View Structure Similarity'. However, it lacks the most important experiments with fully misaligned views. This raises a question: Is the proposed method unable to address the issue of fully misaligned views?\n\n(2) This paper considers the issue of view misalignment. Therefore, performance validation should be compared more with the SOTA methods of view misalignment instead of traditional multi-view clustering methods.\n\n(3) The proposed method uses a feature rotation matrix to align the feature space. Is the rotation matrix effective for the sample, i.e., can the learned rotation matrix be used to output aligned samples?\n\n(4) The presentation of the paper can be further improved. For example, the roles of each component in Eqs. (4), (5) and (7) should be specifically explained.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}