Title: Adversarial Graph Fusion for Incomplete Multi-view Semi-supervised Learning with Tensorial Imputation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Incomplete multi-view learning

graph-based semi-supervised learning
Abstract: View missing remains a significant challenge in graph-based multi-view semi-supervised learning, hindering their real-world applications. To address this issue, traditional methods introduce a missing indicator matrix and focus on mining partial structure among existing samples in each view for label propagation (LP). However, we argue that these disregarded missing samples sometimes induce discontinuous local structures, i.e., sub-clusters, breaking the fundamental smoothness assumption in LP. Consequently, such a Sub-Cluster Problem (SCP) would distort graph fusion and degrade classification performance. To alleviate SCP, we propose a novel incomplete multi-view semi-supervised learning method, termed AGF-TI. Firstly, we design an adversarial graph fusion scheme to learn a robust consensus graph against the distorted local structure through a min-max framework. By stacking all similarity matrices into a tensor, we further recover the incomplete structure from the high-order consistency information based on the low-rank tensor learning. Additionally, the anchor-based strategy is incorporated to reduce the computational complexity. An efficient alternative optimization algorithm combining a reduced gradient descent method is developed to solve the formulated objective, with theoretical convergence. Extensive experimental results on various datasets validate the superiority of our proposed AGF-TI as compared to state-of-the-art methods. Code is available at https://github.com/ZhangqiJiang07/AGF_TI.


Peer reviews:
comments: SUMMARY:
This paper tackles the challenge of missing views in multi-view semi-supervised learning, which often disrupts local data structures and impairs classification accuracy. The authors introduce AGF-TI, a novel framework that combats structural discontinuities by generating a unified graph through adversarial learning. It leverages high-order consistency via tensor-based structure recovery and incorporates anchor points to enhance efficiency. Extensive experiments confirm that AGF-TI consistently outperforms existing methods across multiple benchmarks.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The writing and presentation are good, then the readers can quickly grasp the core idea of the paper.
2. The experiments are sufficient to verify the effectiveness of the proposed method.

Weaknesses:
1. The anchor learning and low-rank tensor decomposition have been widely studies in multi-view clustering, I cannot see some insightful observations and interesting idea.
2. The authors demonstrate the Sub-Cluster Problem on a Synthetic dataset. However, this could be artificially crafted. Hence, the such verification should be conducted on real-world datasets. 
3. The most used multi-view datasets are too small, it’s suggested to add more large-scale datasets.
4. The author claims that introducing anchors can decrease the computational burden, then the running time should be compared.

QUESTIONS:
Please refer to the weaknesses.

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

comments: SUMMARY:
This paper proposes AGF-TI, an innovative approach for incomplete multi-view semi-supervised learning, to address the Sub-Cluster Problem (SCP) caused by missing views. By integrating adversarial graph fusion (min-max optimization) and tensorial imputation (low-rank tensor learning), AGF-TI reconstructs robust consensus graphs and recovers missing structures. Combined with an anchor-based strategy for computational efficiency, AGF-TI demonstrates superior performance on various datasets, validating its effectiveness in handling both view incompleteness and label scarcity challenges.

STRENGTHS AND WEAKNESSES:
Strengths:
1. This paper identifies the Sub-Cluster Problem in graph-based multi-view semi-supervised learning, an important but previously under-explored challenge.
2. AGF-TI innovatively combines min-max scheme with tensorial imputation, forming a robust framework to address SCP. 
3. Clear and mathematically rigorous formulation of the optimization problem, with a solid convergence analysis in the appendix.
4. Experimental results demonstrate a substantial improvement in classification performance. The paper also includes a detailed ablation study to demonstrate the effectiveness of each component in AGF-TI.

Weaknesses:
1. Although the time complexity is discussed, it would be better to provide the running time results of the AGF-TI and baselines for comparison.
2. What does the parameter $\beta$ mean in Eq. (3)?
3. How are the permutation matrices $\mathbf{T}_v$ initialized in practice?

QUESTIONS:
See the weaknesses.

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

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes Adversarial Graph Fusion with Tensorial Imputation (AGF-TI) for Incomplete Multi-view Semi-supervised Learning. This paper addresses a newly identified issue termed Sub-Cluster Problem (SCP). SCP arises when missing views in multi-view data disrupt the smoothness assumption of label propagation by creating disconnected sub-clusters. AGF-TI addresses SCP through i) an adversarial graph fusion scheme leveraging a min-max optimization framework to fuse view-specific graphs and 2) a tensorial imputation mechanism that recovers missing similarity information using high-order correlations. It also employ acceleration strategy to reduce the computational cost.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\n\n1. Extensive experiments on six public datasets under various missing-view and label-scarcity settings show that AGF-TI consistently outperforms state-of-the-art methods in terms of accuracy, precision, and F1-score.\n2. The paper offers a comprehensive optimization algorithm combining reduced gradient descent and ADMM, supported by theoretical convergence analysis.\n\nWeaknesses\n\n1. The experimental protocol creates missing views by randomly selecting V% of the samples and randomly deleting 1 to V – 1 of their views. In Figure 1, however, the Sub-Cluster Problem is illustrated as arising when whole groups of neighboring samples are missing, leaving a \"vacuum region\" that fractures a class manifold into multiple sub-clusters and breaks the smoothness assumption of label propagation. If missing views are indeed independent and spatially random, contiguous voids, thus SCP should occur far less frequently. Could the authors quantify how often SCP actually appears under the current random-missing setup, or ii) provide experiments with more realistic, block-missing / structured-missing patterns to show that AGF-TI remains effective in scenarios where SCP is genuinely likely to occur?\n2. What is the computational and memory footprint of the tensor nuclear-norm step when the number of views, anchors, and samples all scale simultaneously? Specifically, can the authors validate them on a truly large-scale (>100 k samples) benchmark?\n3. The related-work section should discuss recent missing-view representation methods, such as \"COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction\" and \"Decoupled Contrastive Multi-view Clustering with High-order Random Walks.\"\n4. It would be better to give the full name of TNN in Definition 1.\n5. The evaluation lacks deep learning baselines. Benchmarking only against classical or shallow methods limits the paper’s relevance to modern multi-view semi-supervised learning. \n6. In Definition 1, please spell out TNN’s full name Tensor Nuclear Norm.\n\nQUESTIONS:\nIf the authors could address the above weaknesses during rebuttal, I would like to keep my rating.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel method, Adversarial Graph Fusion with Tensorial Imputation (AGF-TI), to address the Sub-Cluster Problem (SCP) in graph-based multi-view semi-supervised learning (GMvSSL) where missing data can distort local structures and degrade classification performance. AGF-TI utilizes an adversarial min-max framework to learn a robust consensus graph that is resilient to these distortions. Simultaneously, it recovers missing structural information by stacking similarity graphs into a tensor and leveraging high-order correlations for imputation. The authors conduct extensive experiments to validate the effectiveness of AGF-TI in handling scenarios with both missing views and limited labeled data.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1.The paper is well-written, and the motivations are clearly described. The identification and formalization of a previously unaddressed challenge in this field, SCP, provide a strong motivation and a clear target for the proposed method.\n\n2.The AGF-TI model is the first to adopt a min-max framework for graph fusion, which is a novel contribution to GMvSSL. Incorporates an anchor-based strategy to reduce the computational cost, making AGF-TI scalable to larger datasets.\n\n3.I enjoyed the significance test in Tables 5, 6, and 7 in Appendix. The provided code and sufficient information make it easy to reproduce the experimental results.\n\nWeaknesses:\n\n1.The AGF-TI model is inherently complex, involving a tri-level max-min-max optimization problem.\n\n2.The theoretical convergence relies on the sufficient optimization assumption.\n\nQUESTIONS:\n1.The central formulation is a max-min-max problem. Could the authors elaborate on the intuition behind this structure? Specifically, how does the outer max over the view weights $\\alpha_v$ in conjunction with the inner min over the fused graph $P$ lead to a more robust consensus graph than a simpler min-min formulation, as seen in methods like CFMSC?\n\n2.Please clarify how the permutation matrix $T_v$ is initialized.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}