Dual-level Affinity Induced Embedding-free Multi-view Clustering with Joint-alignment
Mulit-view Clustering;Large-scale Clustering;Anchor Clustering
Yes
No ethics review needed.
1.	What is the difference between the anchor alignment module with those of existing works?
2.	Why do some compared methods exhibit extremely poor performance on some datasets?
1.	The paper is well-structured, and the authors conduct a relatively comprehensive review on existing literatures.
2.	The experimental results demonstrate the effectiveness of the work
In this work, a multi-view clustering method with joint anchor alignment was developed, which introduces dual-level affinity and achieves embedding-free clustering. The work is designed to address several problems due to the anchor misalignment issues. Therefore, the authors introduce a permutation mechanism for each view to jointly adjust the anchors. Besides, the method is free of learning the embedding by constructing the cluster labels directly from original samples. A self-expression learning structure is utilized on the anchors, which utilizes topology learning strategy to feed captured anchor-anchor features into anchor-sample graph. Extensive experiments validate the effectiveness of the proposed method.
1.	A core idea of the work is to introduce an anchor permutation matrix, while this idea has been widely adopted by previous works. Hence, the novelty of the paper might not be sufficient to be published.
2.	The comparison methods lack some latest works. Since the work is an anchor alignment based method, more related works with anchor alignment should be compared. For example, the reference Liu 2024 (in line 581) was discussed in this paper, which includes anchor alignment mechanism, but it is not compared with the proposed work.
3.	In Table 1, several compared methods exhibit extremely poor performance on some datasets (e.g., PMSC on Cora, AMGL on DeRMATO). It might be better if the authors could explain the possible reasons.
4.	Table 5 does not include all the symbols. The Methodology section might be too brief, which should be introduced with more details by explaining the reasons for the design of each component.
Yes
No ethics review needed.
Same as weaknesses section
1. The model’s dual-level affinity mechanism effectively captures both anchor-sample and anchor-anchor relationships, enhancing clustering accuracy by leveraging a fuller view of the data structure.
2. The flexible joint-alignment method addresses anchor misalignment issues without requiring a fixed baseline view, making the model versatile for clustering data from different sources.
3. The model's effectiveness is demonstrated through comprehensive evaluation on multiple datasets, highlighting its adaptability and strong performance across different data types and views.
This paper presents the DLA-EF-JA model, a multi-view clustering technique that leverages dual-level affinity to capture both anchor-sample and anchor-anchor relationships within data. The model introduces a joint-alignment mechanism to address the anchor misalignment problem across views, which eliminates the need for a baseline view. Unlike traditional embedding methods, DLA-EF-JA generates cluster labels directly, reducing variance and improving clustering stability. Extensive experiments across diverse datasets demonstrate that the proposed model achieves competitive performance compared to existing multi-view clustering methods.
1. **Limited Learning of Cross-View Complementarity:** While the model integrates anchor relations, it lacks complex constraints like the Schatten p-norm that could help capture deeper cross-view complementarities. This may limit the model’s ability to fully leverage unique, complementary information in views with highly distinct features or dimensions. How does the model handle scenarios where the quality of anchors varies significantly across different views?

2. **Necessity of Anchor Alignment:** The reliance on anchor alignment to maintain cross-view consistency introduces additional computational steps. Although this approach appears beneficial, some recent multi-view clustering methods successfully avoid alignment through feature space fusion or shared representations. It would be useful for the authors to elaborate on the essential role of anchor alignment in this model and under what conditions it might be adapted or simplified. Are there specific conditions or datasets where the necessity of anchor alignment might be relaxed or modified?

3. **Complexity of the Model:** The model is somewhat complex, introducing more variables and mathematical processes. A more detailed explanation of the transition from Equation 2 to Equation 3 would enhance reader understanding of the methodology.

4. **Hyperparameter Tuning Requirement:** The model’s performance is sensitive to carefully tuned hyperparameters, such as λ and β. Can the authors provide further insights into the potential effects of anchor noise and how it could be mitigated to improve robustness?
Yes
No ethics review needed.
The authors should check the data since some methods, such as OrthNTF and GSC, since the performance of these new methods is very poor.
(1)  The proposed method considers the affinity relationship between anchors.
(2) The proposed method devises a joint-alignment mechanism that not only eliminates the need for selecting the baseline view but also coordinates well with the generation of anchors.
(3) The proposed method has linear complexity for the loss function.
This paper aims to address these problems: (1) they existing methods focus only on the affinity relationship between anchors and samples, while overlooking that between anchors; (2) the cluster order is inconsistent across views and accordingly anchors encounter misalignment issue due to the lack of data labels. The proposed method explicitly exploits the geometric properties between anchors via self-expression learning skill, and utilizes topology learning strategy to feed captured anchor-anchor features into anchor-sample graph so as to explore the manifold structure hidden within samples more adequately. Experiments on multiple publicly available datasets confirm the effectiveness of the proposed method.
(1)  The novelty of this work is limited since the involved components have been widely used for anchor learning and spectral clustering. The authors only perform these components on the anchor data.
(2) The authors do not compare the proposed method with theses popular deep learning ones.
Yes
No ethics review needed.
See weaknesses.
1.	This paper is easy to read.
2.	Extensive experiments are conducted to show the effectiveness of the method as well as efficiency.
This paper proposes a dual-level affinity induced embedding-free multi-view clustering method with joint alignment, called DLA-EF-JA. Based on previous anchor based multi-view clustering, it further considers the relations among anchors by learning an affinity matrix that are used to guide the anchor matrix learning with graph Laplacian. The multi-view anchors are adaptively aligned. The discrete cluster indicator is also jointly learned.
1.	The novelty of this paper is incremental. The authors consider the relations among samples by self-expression affinity learning, and add a graph based Laplacian for anchor matrix regularization. However, the self-expression affinity learning and graph based Laplacian are widely used in existing subspace clustering works. It also remains unclear why the anchor self-expression enhances the quality of anchors.  

2.	Why learn an anchor affinity matrix $S_p$ for each view separately? It seems to overlook inter-view interactions. Why not directly learn a consensus anchor affinity matrix? Will it improve the performance?

3.	How do you set the number of anchors $k$? What is the influence of it?

4.	The experimental results are not convincing. For instance, OrthNTF achieves 69.4% Acc and 68.6% NMI values on the Reuters dataset, while this paper only reports 28.67% Acc and 3.07% NMI.
@inproceedings{
anonymous2024duallevel,
title={Dual-level Affinity Induced Embedding-free Multi-view Clustering with Joint-alignment},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=58T7xcTxJD},
note={under review}
}
Despite remarkable progress, there still exist several limitations in current multi-view clustering (MVC) techniques. Specially, they generally focus only on the affinity relationship between anchors and samples, while overlooking that between anchors. Moreover, due to the lack of data labels, the cluster order is inconsistent across views and accordingly anchors encounter misalignment issue,  which will confuse the graph structure and disorganize cluster representation. Even worse, it typically brings variance during forming embedding, degenerating the stability of clustering results.   In response to these concerns, in the paper we propose a MVC approach named DLA-EF-JA. Concretely, we explicitly exploit the geometric properties between anchors via  self-expression learning skill, and utilize topology learning strategy to feed captured anchor-anchor features into anchor-sample graph  so as to explore the manifold structure hidden within samples  more adequately.  To reduce the misalignment risk, we introduce a permutation mechanism for each view to jointly rearrange anchors according to respective view  characteristics. Besides not involving selecting the baseline view, it also can coordinate with anchors in the unified framework and thereby facilitate the learning of anchors.  Further, rather than forming embedding and then performing spectral  partitioning, based on the criterion that samples and clusters should be hard assignment, we manage to construct the cluster labels directly from original samples using the binary strategy,  not only preserving the data diversity but avoiding variance. Experiments on multiple publicly available datasets confirm the effectiveness of our DLA-EF-JA.
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Mulit-view Clustering Large-scale Clustering Anchor Clustering
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Dual-level Affinity Induced Embedding-free Multi-view Clustering with Joint-alignment
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