From: ICLR.cc/2023/Conference/Paper786/Authors
title: Submission Withdrawn by the Authors

From: ICLR.cc/2023/Conference/Paper786/Reviewer_VN69
confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
summary_of_the_paper: This paper studies the problem of missing view completion for missing multi-view data. Two Transformer modules are proposed to build a view recovery and clustering framework. An improved recurrent graph constraint is developed to enhance the clustering performance. The authors performed extensive experiments to demonstrate the effectiveness of the method.
strength_and_weaknesses: The strengths of the paper:

1.The paper is well-written, with nice motivations and clear descriptions. 

2.It is a wonderful idea to recover the missing views with the fused representation of multiple views.

3.This paper shows many visualizations of the restoration results, which have a strong visual impact.

4.From the results, data recovery does help clustering to achieve better performance, which may be generalized to other applications, such as classification tasks.

5.A problem, which is often overlooked in deep multi-view learning, that is, when training in small batches, the construction of cross-sample relationships is often degraded, is well considered, or considered to some extent.

The weaknesses of the paper:

1.In Figure 4, the legend of (h) may be from 1 to 10 to match the other examples.

2.To improve reproducibility, implementation details should be provided, such as the optimizer, the learning rate, and other settings that are important to reproduce the model and experiments.

3.More concerns:
In the two stages, the recovered data is only utilized in the generation process of the graph. So why the imputation data is not directly used as new input of the model? On the other hand, since the final clustering representation is Z, is it possible to perform only the training in stage 1?


clarity,_quality,_novelty_and_reproducibility: I think this paper is easy to read and understand, the schematic is clear and unambiguous. The core idea is interesting and novel. As mentioned above, providing more details or code would improve the reproducibility of this paper.
summary_of_the_review: To sum up, the paper has a good organization and story. The innovations/contributions are clear and significant. However, there are still some concerns and drawbacks that need to be addressed.
correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
empirical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
flag_for_ethics_review: NO.
recommendation: 8: accept, good paper

From: ICLR.cc/2023/Conference/Paper786/Reviewer_3yps
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: This paper develops a missing multi-view restoration model for the incomplete multi-view clustering tasks, which aims to recover the miss data via existing multi-view information. A recurrent structure-aware loss and transformer-style auto-encoder framework are designed for better completion and clustering. In addition, this paper focuses on local graph constraint under mini-batch deep learning. Experimental results on five databases confirm the advantages to other methods. 
strength_and_weaknesses: The submission has the following strengths to it: (1) This is a meaningful exploration on the topic of missing multi-view data recovery with more than two views. (2) It's exceedingly well written with explicit problem definition and method description, and it’s readily understandable even without prior knowledge. 
Weaknesses and my concerns over this submission: (1) From my understanding, the recovered data should be used immediately, i.e., the completed data generated in the (k-1)-th epoch should be used as the input in the k-th epoch instead of using the original data to continue the training in the k-th epoch. I believe the authors may have made such an attempt, then why did it be abandoned?
(2) In the ablation experiment, why is the gap between the two schemes B and B+C so different in the Handwritten and NH_face datasets? (3) Missing view recovery is interesting. However, the authors do not provide the key code. In addition, the link to the new constructed dataset Aloi_deep is not provided.

clarity,_quality,_novelty_and_reproducibility: All symbols are well explained and illustrated, and detailed illustration diagrams are provided. If the code and datasets are made public, it will better improve reproducibility and increase the value of community research.
summary_of_the_review: To some extent, this work is innovative and valuable. If my concerns can be addressed, it will be a good step forward for this paper. 
correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
empirical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
flag_for_ethics_review: NO.
recommendation: 6: marginally above the acceptance threshold

From: ICLR.cc/2023/Conference/Paper786/Reviewer_4vaY
confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
summary_of_the_paper: - The paper aims to complete the missing multi-view data by an autoencoder network named SMVC. 
     - The paper combines the characteristics of the multi-head self-attention mechanism and multi-view learning to design a transformer-style cross-view autoencoder as the base framework. 
     - Then, to mine the geometric structure between samples, a structure-aware loss is employed to the multi-view feature extraction module. 
     - Finally, this paper gives convincing experimental results including real completion effects. 
strength_and_weaknesses: ### Strengths:
- Whether it is for clustering or other downstream tasks, incomplete multi-view data recovery is necessary. Previous works, especially those aimed at recovering missing views directly, are mostly oriented to datasets containing only two views rather than true multiple views. This work can not only handle these datasets with more than two views, but also adapt to the datasets without requiring complete samples, which is more suitable for real application scenarios.
- For incomplete data, graph learning is limited due to incomplete neighbor relations. It is a good idea to construct an approximately complete graph from recovered data.
- Although this is a two-stage model, the authors do a good job of describing the components, especially the motivation section.

### Weaknesses:
- As far as I know, the value of K is important for the graph constructed by the KNN algorithm, which I believe is directly related to the performance of the SMVC, but there is no discussion of the K in the context.
- The conclusion is only a summary of the whole paper, even similar to the abstract, I believe it would be more perfect to add thinking about the extension or prospect of the method.

### Other question:
In addition to the above comments, I have one question to discuss with the authors：As mentioned in Section 3.4, graph $G$ is fixed in the second stage, why not continuously update the graph as in the first stage?
clarity,_quality,_novelty_and_reproducibility: - The clarity and quality of the paper are good. 
- The topic and approach are also novel. 
- Reproducibility depends on the authors providing more implementation details and code in the future, which I am looking forward to seeing.
summary_of_the_review: - As can be inferred from the “Strength and Weaknesses”, this paper has good intuitive and technical insights. 
- I think this work is beneficial to the multi-view learning community. Hopefully, the authors can address my concern in the rebuttal period.
correctness: 4: All of the claims and statements are well-supported and correct.
technical_novelty_and_significance: 4: The contributions are significant, and do not exist in prior works.
empirical_novelty_and_significance: 4: The contributions are significant, and do not exist in prior works.
flag_for_ethics_review: NO.
recommendation: 8: accept, good paper

From: ICLR.cc/2023/Conference/Paper786/Reviewer_Cu9o
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: This paper proposes a general incomplete multi-view clustering model with a two-stage training strategy that can handle all kinds of missing cases. Unlike most existing methods, the approach focuses on recovering missing views and performing complete multi-view clustering based on the imputation. A loop-generated k-NN graph is introduced in the model to preserve the local structure of the data. 
strength_and_weaknesses: # Strengths: 
- The paper is well-written and easy to follow. 
- This paper captures an easily overlooked problem in incomplete multi-view learning, which is to recover missing data rather than ignore it. I think the study of this problem is valuable.
- It is an interesting idea to use recovered data to generate a new graph and cyclically facilitate representation learning.

# Weaknesses:
- There is a lack of explanation for some designs. For instance, what is the motivation for two stages instead of one? 
- How to generate the initial neighbor graph? 
- The “structure-aware” mentioned in the title is not elaborated enough in the context. How to understand the “structure-aware”?

clarity,_quality,_novelty_and_reproducibility: Overall, the work is good, the writing is clear, and the implementation details are ample.
summary_of_the_review: Incomplete multi-view learning has significant practical implications, and using a self-attention mechanism to achieve cross-view fusion is novel. Cyclic missing view completion and graph constraint provide more ideas for related work. The impressive restoration of missing views is enough to confirm that this is good work.
correctness: 4: All of the claims and statements are well-supported and correct.
technical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
empirical_novelty_and_significance: 4: The contributions are significant, and do not exist in prior works.
flag_for_ethics_review: NO.
recommendation: 8: accept, good paper