Title: Incomplete Multi-view Deep Clustering with Data Imputation and Alignment
Venue: NeurIPS 2025
Accepted: yes
Keywords: multi-view clustering

deep clustering

incomplete clustering
Abstract: Incomplete multi-view deep clustering is an emerging research hot-pot to incorporate data information of multiple sources or modalities when parts of them are missing. Most of existing approaches encode the available data observations into multiple view-specific latent representations and subsequently integrate them for the next clustering task. However, they ignore that the latent representations are unique to a fixed set of data samples in all views. Meanwhile, the pair-wise similarities of missing data observations are also failed to utilize in latent representation learning sufficiently, leading to unsatisfactory clustering performance. To address these issues, we propose an incomplete multi-view deep clustering method with data imputation and alignment. Assuming that each data sample corresponds to a same latent representation among all views, it projects the latent representations into feature spaces with neural networks. As a result, not only the available data observations are reconstructed, but also the missing ones can be imputed accordingly. Moreover, a linear alignment measurement of linear complexity is defined to compute the pair-wise similarities of all data observations, especially including those of the missing. By executing the above two procedures iteratively, the discriminative latent representations can be learned and used to group the data into categories with off-the-shelf clustering algorithms. In experiment, the proposed method is validated on a set of benchmark datasets and achieves state-of-the-art performances.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nExisting multi-view deep clustering methods encode the data observations into latent representations but ignore their uniqueness to all views. Also, they fail to consider the pair-wise similarities of missing data observations. To address the two problems, this paper proposes an incomplete multi-view deep clustering method with data imputation and alignment named IMDC-DIA.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n\n1. Novelty. Different from the existing methods of encoding the data observation into latent representation, this paper assumes the latter is unique and projects it to reconstruct the former. This is rational and novel. \n\n2. Writing. This paper is well-organized and easy to follow. Also, the framework of Fig. 1 provides a good overview of the method and is appreciated.\n\n3. Effectiveness. The proposed method achieves the best performances over those in literature in almost all missing ratios. Also, the ablation study well validates effectiveness of the data imputation and alignment modules.\n\n\nWeakness\n\n1. Completeness of experiment. This paper lacks some experiment results. \n\n2. Clarity of experiment setting. Some of the important experiment settings, such as details of deep learning structure, are missing. \n\n- Please find the specifics in Questions section.\n\nQUESTIONS:\n1. In the experiment setting, the authors claim that four metrics are adopted to evaluate the method. But there are only two of them, i.e. ACC and NMI, in the tables. So the authors are supposed to provide the results of PUR and ARI.\n\n2. The missing data setting should be illustrated more clearly. \n\n3. More details of experiment settings are expected to be provided, such as the neural network structure, learning rate, optimizer, etc. \n\n4. The code should be released publicly for reproductivity.\n\nLIMITATIONS:\nYes, at the end of the Conclusion section.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nTo consider the uniqueness of data representation of all data views and the pair-wise similarities of missing data observations, the authors propose an incomplete multi-view deep clustering method by imputing the missing data and aligning them iteratively. Moreover, they conduct experiments on the method and achieve state-of-the-art performances.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n- The authors propose a new measurement (linear alignment) similar to kernel alignment which reduces the computation complexity to O(n). Also, the linear alignment is validated to be effective in experiments, especially in ablation study of Table 2. This will have a broader impact on the future research. \n\n- The experiment design of ablation study is impressive. Insides, the w/o imp. and w/o align. settings well validate the functions of data imputation and data alignment components respectively. Also, the w/o imp. setting considers four strategies, i.e. the average-filling, zero-filling, rand-filling and no-filling, which is complete. \n\n- The IDMC-DIA method achieves state-of-the-art results compared with the existing methods in literature, well validating its effectiveness.\n\n- The limitations are discussed. Also, the possible future work is also provided, which is appreciated. \n\nWeaknesses\n\n- As seen from Fig. 1 and corresponding descriptions, the $z_i^{(v)}$ in Equation (1) is supposed to be $z_i$ probably. The authors should check this.\n\n- In Equation (3), $v$ lacks the bracket. Please keep consistent in the whole paper.\n\n- In optimization section, the authors provide the method to optimize the neural network parameter, latent representation and weight parameter. But how to combine them in order is not illustrated clearly. The authors are encouraged to provide the pseudocode. \n\n- The convergence should be analyzed. \n\n- Table 1 and 2 only present the ACC and NMI results, but miss the PUR and ARI. Although the paper length is limited, the authors may provide them in Appendix.\n\nQUESTIONS:\nMost issues have already been mentioned in Weaknesses. One additional problem is as follows:\n- In Theorem 1, the authors say \"The linear alignment of two arbitrary matrices measures the consistency of the pair-wise linear similarities of their rows,\" but they do not define what \"consistency\" means or \"pair-wise linear similarities\". The authors should clearly define \"pair-wise linear similarities\" and \"consistency\" in Theorem 1 and Proof 1.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes an incomplete multi-view deep clustering method with data imputation and alignment named IMDC-DIA. It assumes that the latent representations are unique to a fixed set of data samples in all views. Moreover, it utilizes the pair-wise similarities of missing data observations in latent representation learning. In experiments, the authors compare it with the existing methods on four datasets. It can be seen that the IMDC-DIA method obtains state-of-the-art performances.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n1. The proposed method assumes that the latent representations are unique to a fixed set of data samples in all views, which is different from that of the most existing methods. This is rational and more consistent to real-world practice.\n\n2. The proposed linear alignment considers but does not require to compute the pair-wise similarities of data samples between different views, hence is of linear complexity to sample number. This is novel and practical, especially in large-scale problem. \n\n3. The proposed method is only composed of three simple and direct modules, i.e. data reconstruction, imputation and alignment. No sophisticated techniques are derivations are involved. But it achieves impressive performances in the comparison experiments. So the method is elegant and effective. \n\n4. The paper is well-organized and the writing is easy to follow.\n\n**Weaknesses**\n\n1. In Eq. (9), the trade-off parameter is $\\alpha$, but the parameter is written to $\\beta$ mistakenly in Fig. 2 and corresponding analysis. Please keep them consistent. \n\n2. In experiment, the performance and loss to training epoch are supposed to be presented and analyzed, at least for validating the rationality of the loss design. \n\n3. It is claimed that PUR and ARI are adopted in experiments in Section 4.1. However, they are missing in Table 1 and 2.\n\n4. Fig. 2 presents the accuracy variation to parameter $\\beta$. Also, the other evaluation measurements should be provided, including NMI, PUR and ARI.\n\n5. The optimization should be enhanced. At least, the optimization order and logic should be clarified.\n\n6. Please eliminate all typos and grammar errors.\n\n7. In Theorem 1, the authors say \"The linear alignment of two arbitrary matrices measures the consistency of the pair-wise linear similarities of their rows,\" but they do not define what \"consistency\" means or \"pair-wise linear similarities.\" So, Theorem 1 does not make sense at all in the current manuscript.\n\nQUESTIONS:\nSee strengths and weaknesses.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nExisting incomplete multi-view deep clustering methods typically encode each data view into an independent latent representation, overlooking the intrinsic uniqueness of the underlying data representation across views. Moreover, they fail to sufficiently exploit the pair-wise similarities among missing data instances. To address these limitations, this paper proposes a novel incomplete multi-view deep clustering approach that jointly imputes missing data and aligns the reconstructed views using a proposed linear alignment mechanism. The method enforces consistency across views by assuming a shared latent representation for each sample and projects it to each view via independent neural networks. Extensive experiments on four benchmark datasets demonstrate that the proposed approach consistently outperforms state-of-the-art methods across a wide range of missing ratios, validating its effectiveness in both representation learning and clustering accuracy.\n\nSTRENGTHS AND WEAKNESSES:\n# Strength\n\n1. The proposed linear alignment measurement is simple but effective and efficient (linear complexity) to incorporate the pair-wise similarities of missing data.\n\n2. The proposed IMDC-DIA achieves promising clustering results compared to the existing methods.\n\n3. The limitations are discussed at the end of *Conclusion* section.\n\n# Weaknesses\n\n1. The authors are expected to doubly check the Equation (1). This paper argues that the latent representation is unique to all data views. But $z_i^{(v)}$ in Equation (1) indicates that each data view corresponds a latent representation. The authors are encouraged to clarify this ambiguity and revise the notation accordingly. It is also recommended to carefully proofread the entire manuscript to eliminate typos and inconsistencies.\n\n2. Although the paper reports accuracy and normalized mutual information (NMI), purity is another standard metric in clustering evaluation. Including purity results (if available) would provide a more comprehensive performance comparison.\n\n3. In ablation study, \"Three settings are designed ...\". However, only two settings are designed and tested.\n\n4. The convergence should be analyzed empirically at least.\n\n5. The paper focuses on data imputation as a core component, yet recent related works on imputation for incomplete multi-view clustering are not cited, e.g., Incomplete Multi-view Clustering via Prototype-based Imputation, IJCAI 2023, Decoupled Contrastive Multi-view Clustering with High-order Random Walks, AAAI 2024.\n\n6. Theorem 1 plays a central role in the paper’s theoretical foundation, but its statement lacks clarity. Specifically, key terms such as \"consistency\" and \"pair-wise linear similarities\" are not formally defined, making the theorem difficult to interpret or evaluate.\n\nQUESTIONS:\nPlease find in *Strengths and Weaknesses* section.\n\nLIMITATIONS:\nyes, at the end of *Conclusion* section.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-06-27"}