Reliable Attribute-missing Multi-view Clustering with Instance-level and feature-level Cooperative Imputation

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-04 17:21:42

Abstract: Multi-view clustering (MVC) constitutes a distinct approach to data mining within the field of machine learning. Due to limitations in the data collection process, missing attributes are frequently encountered. However, existing MVC methods primarily focus on missing instances, showing limited attention to missing attributes. A small number of studies employ the reconstruction of missing instances to address missing attributes, potentially overlooking the synergistic effects between the instance and feature spaces, which could lead to distorted imputation outcomes. Furthermore, current methods uniformly treat all missing attributes as zero values, thus failing to differentiate between real and technical zeroes, potentially resulting in data over-imputation. To mitigate these challenges, we introduce a novel Reliable Attribute-Missing Multi-View Clustering method (RAM-MVC). Specifically, feature reconstruction is utilized to address missing attributes, while similarity graphs are simultaneously constructed within the instance and feature spaces. By leveraging structural information from both spaces, RAM-MVC learns a high-quality feature reconstruction matrix during the joint optimization process. Additionally, we introduce a reliable imputation guidance module that distinguishes between real and technical attribute-missing events, enabling discriminative imputation. The proposed RAM-MVC method outperforms nine baseline methods, as evidenced by real-world experiments using single-cell multi-view data.

Main paper: 
 related work: 
 During the data collection process, a significant portion of multiview data often experiences the loss of specific views or values due to technical limitations. The MVC method seeks to leverage the consistency and complementarity of features across multiple views to address the missing data, thus mitigating the impact of incomplete data on clustering performance. In this section, we will categorize and review the existing missing MVC methods into two types: Instance-missing MVC Methods and Attribute-missing MVC Methods.In terms of instance absence, Liu et al. redefined the issue of missing instances as a challenge of completing incomplete view similarity graphs [28] , successfully yielding discriminative representations. [29] . They compressed all instances into a shared space, constructed cross-view similarities, and continuously optimized the constructed similarity matrix. This approach achieved excellent results in addressing the incomplete multi-view clustering challenge.Regarding attribute absence, Peng et al. employed graph diffusion techniques to enhance the consistency of node embeddings across two views [35] , thus facilitating attribute imputation within the input space of graphs. However, their method remains operational at the instance level, without the ability to modify attribute values. Pu et al. developed an adaptive imputation layer for handling missing multi-view data [36] ; the layer constructs upon the results of soft clustering across multiple views, and is integrated with global cluster centroids. Nonetheless, it primarily addresses missing instances. Yu et al. introduced the concept of value-level missing. They performed a simple matrix correction to allow the existing MVC method to be applicable for incomplete MVC data, but did not delve into further research on missing attributes [51] . Different from these methods, Wu et al. 's team constructed a generative adversarial model based on optimal transmission theory to enhance the interpolation process [44] . This interpolation method was proven effective, yet further exploration of the clustering framework was not pursued. Despite these advancements, no multi-view clustering model has been specifically designed to address missing attributes. Furthermore, current methods neglect the potential synergistic effects between the instance and feature spaces.  
proposed method: 
 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 ...The framework for the proposed RAM-MVC. This model is composed of two main modules: 1) A Reliable Imputation Guidance Module that distinguishes between real and technical missing events during the imputation process. 2) A Bi-Level Cooperative Imputation Module that reconstructs features based on structural information from both the instance and feature spaces.Table 1 : Symbol Appointment Symbol DescriptionThe dimension in the 𝑝-th view.The number of the instances.The zero expression rate in the 𝑘-th cluster.The mean value in the 𝑘-th cluster.The variance value in the 𝑘-th cluster.The confidence level of zero value.X (𝑝 ) ∈ R 𝑛×𝑑𝑝The raw data of the 𝑝-th view.H (𝑝 ) ∈ R 𝑛×𝑑𝑝The reliable imputation guidance matrix of the 𝑝-th view.W (𝑝 ) ∈ R 𝑑𝑝 ×𝑑𝑝The feature transforming matrix of the 𝑝-th view.The similarity matrix.L 𝑠 ∈ R 𝑛×𝑛The Laplacian matrix.The transpose of matrix.Frobenius norm.The trace of matrix.The weight coefficient of the 𝑝-th view.Regularization parameters.Confidence threshold.as{X (𝑝 ) ∈ R 𝑛×𝑑 𝑝 } 𝑣 𝑝=1 , where X (𝑝 ) = {x (𝑝 ) 1 ; ...; x (𝑝 )𝑛 }, and 𝑑 𝑝 denotes the dimension in the 𝑝-th view. The Frobenius norm of X (𝑝 ) is expressed as∥X (𝑝 ) ∥ 𝐹 = √︂ 𝑛 𝑖=1 𝑑 𝑝 𝑗=1 (𝑥 (𝑝 )𝑖 𝑗 ) 2 , while Tr(X (𝑝 ) ) and X (𝑝 ) ⊤ respectively represent the trace and transpose of X (𝑝 ) . Symbols used in our paper can be referenced in Table 1 .There are two potential types of attribute-missing events: 1) Real missing events, which refer to the non-expression of features in instances, resulting in zero values, and 2) Technical missing events, which refer to the loss of attributes during the data collection process due to limitations of technologies. Here, we aim to retain the real missing events as they provide insights into real-world facts, while recovering the technical zero values to address the information loss.To differentiate between these two types of attribute missingness, confidence levels for each feature 𝑙 (𝑘 ) 𝑗 are computed according to scImpute [19] . Specifically, zero values for features exhibiting high expression and low variability within instance clusters are considered technical zeros and require recovery. Conversely, features that consistently exhibit low levels of expression and demonstrate high variability are identified as real zeros, reflecting important insight and requiring preservation. The formula for calculating the confidence levels 𝑙 (𝑘 ) 𝑗 is provided below:EQUATIONwhere 𝑘 represents the cluster number, while 𝑧 The reliable imputation guide matrix H is computed based on the confidence levels 𝑙 (𝑘 ) 𝑗 . The process is as follows:EQUATION)where 𝑡 denotes the confidence threshold established to prevent excessive imputation. Guiding matrix H corresponds in dimensions to the numerical matrix X, with elements populated exclusively by values of 0 and 1. These indicate whether missing values should be imputed at corresponding locations in X. If the confidence level falls below 𝑡, a value in matrix H is set to 1, thus protecting the real zero values from imputing.When missing features appear in the observed view, utilizing existing features to infer the missing ones represents a simple and intuitive idea. Therefore, we propose extending the concept of selfrepresentation to the feature level. Specifically, we posit that each feature can be reconstructed via a linear combination of other features. Given the multi-view dataset X (𝑝 ) ∈ R 𝑛×𝑑 𝑝 , we present the mathematical formulation as follows: minEQUATIONwhere W (𝑝 ) ∈ R 𝑑 𝑝 ×𝑑 𝑝 denotes the projection matrix for the 𝑝-th view, used to map the original data to a new feature space. Each row in W (𝑝 ) signifies the contribution of each feature to the reconstruction, relative to other features. In essence, this matrix can illustrate the significance of each feature. Furthermore, orthogonal constraints W (𝑝 ) ⊤ W (𝑝 ) = I are applied to W (𝑝 ) to promote feature independence, helping to eliminate redundant information among features. Moreover, limiting the complexity of the transformation matrix will constrain the model's capacity, thereby mitigating the risk of overfitting. In this study, we employ the matrix W (𝑝 ) to facilitate the reconstruction of missing features, thus, the quality of the reconstructed features is deeply contingent upon the quality of W (𝑝 ) .Numerous MVC methods focus on feature recovery by reconstructing instance-level information, with their limitations outlined in our prior discussion. However, these limitations should not be interpreted as a disregard for the importance of the structural information in the instance space. We maintain that structural information in both instance and feature spaces is of equal importance. Their joint optimization facilitates the capture of potential synergistic effects across domains, enabling the model to uncover deeper insights into the missing data. Therefore, in this section, we introduce the construction of structural information at the instance level.Leveraging the foundational principles of spectral theory and manifold learning [9] , the similarity between two instances in the original high-dimensional space signifies their proximity within a specific local neighborhood in that space. Consequently, this similarity ought to be preserved during the dimensional reduction of these data to a lower-dimensional space. In light of the aforementioned principles, we have devised a module to adaptively learn the nearest neighbor graph and obtain the structural information of the instance space, with the corresponding formula expressed as follows:EQUATIONwhere x(𝑝 ) 𝑖,: denotes the i-th row of X (𝑝 ) , while x (𝑝 ) :,𝑖 denotes the i-th column of X (𝑝 ) . The constraints 𝑠 𝑖 𝑗 ≥ 0 guarantee the rationality of the constructed similarity matrix, while the column constraints of 1 ⊤ s :,𝑖 = 1 ensure that the elements of each column in the similarity matrix sum to 1, preventing single data points from disproportionately influencing the overall structure.To facilitate a simpler solution, leveraging the properties of matrix trace, Eq. ( 4) can be transformed into an equivalent form for optimization:EQUATIONwhere L S = I -D -1 2 SD -1 2 represents the Laplacian matrix, S represents the similarity matrix, and D represents the diagonal matrix derived from S.Instance-level similarity has been extensively utilized in feature reconstruction and has achieved widespread verification [40, 49] , whereas interpreting feature-level similarity presents challenges. To provide an intuitive understanding, we use single-cell multi-view data as an example, which regards genes as features and highlights a complex network of interactions among genes.Therefore, we posit that structural information concurrently exists within both instance and feature spaces, and these two spaces possess the potential for synergistic effects. To adaptively learn the structural information from both the instance and feature spaces, we combine Eq. (3) and Eq. ( 5) to construct the bi-level objective function as follows: minEQUATIONwhere 𝛼 𝑝 denotes the weight coefficient of the 𝑝-th view, which is constructed to balance individual views. By optimizing Eq. ( 6), we can effectively learn the structural information at both the instance and feature levels, thus obtaining a more accurate W (𝑝 ) . As we state in Section 3.2, the quality of the reconstructed features is deeply contingent upon the quality of W (𝑝 ) . By enhancing the quality of W (𝑝 ) , we ultimately obtain the high-quality reconstructed feature.Input: Attribute-miss multi-view data {X (𝑝 ) } 𝑣 𝑝=1 ; the parameters 𝛼 and 𝜆; the threshold of imputation guidance 𝑡. 1: Initialize {W (𝑝 ) } 𝑣 𝑝=1 , 𝜶 , and S. 2: Calculate H (𝑝 ) via Eq. ( 2). 3: while not convergent do Update {W (𝑝 ) } 𝑣 𝑝=1 via Algorithm 2;5:Update S by solving Eq. ( 13); Update 𝜶 by solving Eq. ( 16); 7: end while 8: Calculate { X(𝑝) } 𝑣 𝑝=1 via Eq. ( 7); 9: Calculate Ŝ by solving Eq. ( 8); Output: Perform spectral clustering on Ŝ to obtain the final results.1: Initialize W (𝑝 ) * = W (𝑝 ) . 2: while not convergent do 3:B (𝑝 ) = 2 𝛾 𝑚𝑎𝑥 I -A (𝑝 ) W (𝑝 ) * + 2X (𝑝 ) ⊤ X (𝑝 ) ; 4:Perform SVD on B (𝑝 ) as B (𝑝 ) = U ⊤ ΣV; 5:W (𝑝 ) * = U ⊤ V; 6: end while 7: Output: W (𝑝 ) = W (𝑝 ) * .After obtaining high-quality reconstructed features, we refrain from directly imputing them into the corresponding missing features. As previously mentioned, this operation leads to over-imputation issues. Here, we employ the guiding matrix H to perform discriminatory imputation. The imputation process is computed as follows:EQUATIONwhere • represents the Hadamard product, X(𝑝) represents the data after imputation, embodying high-quality features and subsequently utilized for further clustering analysis. Specifically, we recompute the similarity as follows:minEQUATIONThe ultimate clustering result is generated by performing spectral clustering on the refined similarity matrix Ŝ, and the overall procedure is detailed in Algorithm (1).The optimization problem in Eq. ( 6) can be solved using alternating optimization methods, where in each iteration, one variable is fixed while the others are optimized, and this process continues iteratively until the objective function converges. Update W (𝑝 ) with S and 𝜶 fixed. Fixing other variables, the subproblem concerning W (𝑝 ) can be rewritten as: minEQUATIONBy removing irrelevant terms, we independently optimize W (𝑝 ) on each view as follows: minEQUATIONwhere A (𝑝 ) = X (𝑝 ) ⊤ X (𝑝 ) + 𝛼 2 𝑝 X (𝑝 ) ⊤ L S X (𝑝 ) . The above equation can be relaxed as follows, maxEQUATIONwhere 𝛾 𝑚𝑎𝑥 is the largest eigenvalue of A (𝑝 ) . According to reference [34] , Eq. ( 11) can be solved by optimizing the following problem iteratively, maxEQUATIONwhere B (𝑝 ) = 2 𝛾 𝑚𝑎𝑥 I -A (𝑝 ) W (𝑝 ) * + 2X (𝑝 ) ⊤ X (𝑝 ) and W (𝑝 ) * denotes the optimal W (𝑝 ) in the last iteration. By performing SVD decomposition B (𝑝 ) = U ⊤ ΣV, the optimal W (𝑝 ) at each iteration is U ⊤ V.Update S with W (𝑝 ) and 𝜶 fixed. Fixing other variables, the subproblem concerning S can be rewritten as:EQUATIONThe above function can be rewritten as follows, minEQUATION)Considering that each column of S is uncorrelated with each other. Denoting s :,𝑗 as a vector with 𝑠 𝑖 𝑗 to be the 𝑗-th element, Eq. ( 14) can be optimized in column form as follows, min s :,𝑗 1 2 𝜆s ⊤ :,𝑗 s :,𝑗 + e ⊤ :,𝑗 s :,𝑗 , 𝑠.𝑡 .s 𝑖 ≥ 0,EQUATION)where e 𝑖,𝑗 = 14𝜆 𝑣 𝑝=1 𝛼 𝑝 2 ∥x (𝑝 ) 𝑖,: W (𝑝 ) -x (𝑝 )𝑗,: W (𝑝 ) ∥ 2 2 . Following [34] , we can easily get the closed form solution of each s (𝑝 ) 𝑖 .Update 𝜶 with W (𝑝 ) and S fixed. Fixing other variables, the objective function with respect to 𝜶 can be formulated asEQUATIONwhere g 𝑝 = Tr W (𝑝 ) ⊤ X (𝑝 ) ⊤ L S X (𝑝 ) W (𝑝 ) . We can obtain the optimal 𝛼 𝑝 by Cauchy-Buniakowsky-Schwarz inequality as Space complexity analysis. The main matrix variables required to be stored during the computation process of RAM-MVC include the raw data matrix {X (𝑝 ) } 𝑣 𝑝=1 , the imputation guidance matrix {H (𝑝 ) } 𝑣 𝑝=1 , the feature transformation matrix {W (𝑝 ) } 𝑣 𝑝=1 , the similarity matrix S, and the Laplacian matrix L S . Without considering the space occupied by vectors, the space complexity of RAM-MVC is O (𝑛 2 + 𝑣 𝑝=1 (𝑛𝑑 𝑣 + 𝑑 2 𝑣 )).EQUATION 
experiments 4.1 experimental settings: 
 Benchmark Datasets. Through investigation [19, 27] , we find that single-cell multi-view data naturally encompass both technical and real missing events. Therefore, in this study, we have chosen to evaluate our model in a biomedical scenario. Six real-world singlecell multi-view datasets are involved: BMNC-I, BMNC-II, PBMC, SLN111, SMAGE-I, and SMAGE-II. Detailed information about these datasets is provided in Table 2 . The BMNC dataset was sourced from the GEO database, which is a comprehensive archive of highthroughput gene expression data and genomic information [3] .The PBMC, SMAGE-3K, and SMAGE-10K datasets were obtained from the renowned biological database at www.10xgenomics.com. SLN111 originates from the work of Yosef et al. [31] .Competitive Algorithms. We compare the proposed RAM-MVC with the following state-of-the-art approaches, i.e., FMCNOF (Fast Multi-View Clustering via Nonnegative and Orthogonal Factorization) [47] ; FastMICE (Fast Multi-View Clustering Via Ensembles: Towards Scalability, Superiority, and Simplicity) [14] ; RMKM (Multi-View K-Means Clustering on Big Data) [4] ; UOMVSC (Unified One-Step Multi-View Spectral Clustering) [37] ; AMGL (Parameter-Free Auto-Weighted Multiple Graph Learning: A Framework for Multiview Clustering and Semi-Supervised Classification) [32] ; MSGL (Structured Graph Learning for Scalable Subspace Clustering: From Single View to Multiview.) [15] ; DCCA (Deep cross-omics cycle To evaluate the effectiveness of the proposed module, we constructed three variants of RAM-MVC as follows: (1) RAM-MVC w.o. Feature: The feature-level graph learning module was removed from the complete model; (2) RAM-MVC w.o. Instance: The instancelevel graph learning module was removed from the complete model;(3) RAM-MVC w.o. Guidance: The reliable imputation guidance module was removed from the complete model. Fig. 3 presents the results of ablation experiments on BMNC-I and SMAGE-I datasets. We found that the variant RAM-MVC w.o. Guidance experienced significant performance degradation across two datasets, illustrating that the proposed reliable guidance module effectively addresses the discriminative imputation issue, thereby enhancing the clustering performance. Additionally, removing the instance-level and feature-level learning modules significantly degrades the model's performance to varying degrees, which illustrates the effectiveness of the bi-level cooperative imputation module. Fig. 4 presents the similarity matrix corresponding to various variants, indicating that preserving all modules achieves the highest-quality similarity matrix. To summarize, the ablation experiments underscore the effectiveness of our proposed three modules and highlight the superiority of the unified clustering framework integrating reliable guidance and bi-level cooperative imputation.According to the object function in Eq. ( 6), the regularization hyperparameter 𝜆 is incorporated, while the confidence threshold 𝑡 is introduced in Eq. ( 2). Thus, the RAM-MVC model incorporates two hyperparameters: 𝜆 and 𝑡. To investigate the impact of hyperparameters on model performance, comprehensive parameter experiments were conducted on the BMNC-I and SMAGE-I datasets, the result is shown in Fig. 5 . It is observed that across two datasets, fluctuations in 𝜆 significantly affect clustering performance, yet stability can be maintained within a certain range. This indicates that RAM-MVC is sensitive to the 𝜆 parameter, requiring adjustments within an appropriate range for optimal performance. Conversely, 𝑡 does not exhibit the same level of sensitivity as 𝜆. On the SMAGE-I dataset, the parameter 𝑡 has a limited impact on the final performance. However, for the BMNC-I dataset, significant performance fluctuations were observed, demonstrating that the sensitivity of the parameter 𝑡 is related to the dataset's characteristics. Furthermore, to investigate the convergence of RAM-MVC, convergence curves for the BMNC-I and SMAGE-I datasets were plotted, as depicted in Fig. 6 . From the curves, it can be observed that the RAM-MVC algorithm converges after multiple iterations. 
conclusions: 
 In summary, we propose a novel MVC method tailored for attributemissing events, which overcomes the limitations of current methods that indiscriminately treat all missing attributes as zero values and neglect the contributions of bi-level structural information to feature reconstruction. The proposed RAM-MVC model seamlessly integrates reliable guidance and bi-level imputation into a unified learning framework. Instance-level and feature-level structural information is simultaneously leveraged to generate high-quality reconstructed features, and the confidence of zero values is calculated to facilitate discriminatory imputation on missing information, thereby avoiding over-interpolation. Furthermore, we have explored the application of this method in the biomedical field, demonstrating that it effectively completes the missing attribute information in single-cell multi-view data and achieves enhanced clustering performance. Experimental results from six real-world datasets underscore RAM-MVC's superiority over other benchmark methods.  
: 
 biomedical research, social network analysis, and recommendation systems, etc. However, due to technical limitations of collection equipment, complex system environments, and privacy concerns, missing attributes in multi-view data remain a prevalent issue [1, 8] . For instance, within the public biological dataset [2] , over 80% of the collected multi-modal sequencing results for various genes are missing, as is illustrated in Fig. 1 . The missing attributes pose a significant challenge to existing MVC analysis.Existing MVC methods predominantly address issues of instancelevel missingness [18, 20, 21, 23] , referring to incomplete instances within the observed view. Several studies introduce the concept of view-level missingness [12, 25, 39, 43] , which is just a specific case of instance-level missingness that occurs when all samples in a view are missing. These studies typically develop a mapping function between complete and missing views to impute zero values in missing instances. For instance, Goeleven et al. developed separate mapping functions for each of the four complete views to impute information in target views with missing samples [11] . These mappings are constructed based on instance-level similarity, neglecting feature similarity and thus overlooking potential synergies between instance and feature spaces. Furthermore, in real-world multi-view data, cases of missing attributes occur far more frequently than those of missing instances. For example, in patients undergoing physical examinations, data on respiratory function, blood pressure, and electrocardiograms were collected, while urine tests were inadvertently omitted. Existing MVC methods fail to account for missing attributes and are neither effective nor appropriate for addressing the attribute-missing issue. Merely applying the mapping function learned in the instance space for imputation can lead to distorted outcomes.Furthermore, existing MVC methods address missing attributes indiscriminately [5, 38, 48, 52, 53, 56] . However, in practical applications, there are at least two scenarios for missing attributes: real and technical zero values. For instance, one student genuinely scored zero points on an exam, while another earned ninety-nine points but was assigned a score of zero due to the loss of his test paper. Non-discriminatory restoration of zero values can result in students who genuinely scored zero points receiving inflated scores, deviating from actual outcomes. This problem is especially prevalent in biomedical contexts. Single-cell multi-view data frequently feature a significant number of zero values [17, 26, 30] . Some of these zero values signify expression intensities with biological significance, whereas others stem from information loss attributable to technical detection limitations. Indiscriminate imputation can lead to unexpected and uninterpretable outcomes.To address the aforementioned issues, we have developed a novel MVC framework for handling missing attributes, named Reliable Attribute-Missing Multi-View Clustering (RAM-MVC). This model comprises two main modules: 1) A reliable imputation guidance module, which assesses whether imputation should proceed based on the confidence levels of zero values and discriminates between real and technical zero values. 2) A bi-level cooperative imputation module that simultaneously extracts structural information from both feature and instance spaces, thereby enhancing the feature reconstruction process. Fig. 2 depicts the architecture of the proposed RAM-MVC framework. The contributions of this study are summarized as follows:• We propose a pioneering unified attribute-missing MVC framework that seamlessly integrates bi-level imputation with reliable guidance, ensuring both components collaborate effectively to achieve accurate imputation and enhanced clustering performance. • The developed imputation guidance module effectively differentiates between real and technical attribute-missing events, thus addressing the over-imputation problem prevalent in existing MVC methods. Additionally, by leveraging the structural information from both the instance and feature spaces, the Bi-level imputation module jointly optimizes the feature reconstruction matrix and secures high-quality features.  
 
 Appendix:  


Decision: The paper received four reviews. After the rebuttal phase, the two reviewers who commented abut the rebuttal document stated that their doubts had been clarified,  and at the end one reviewer recommends borderline accept, one accept, one weak accept, and one borderline reject.
Given the fact that 3 reviewers out of 4 tend to be positive,  and the most negative one (still not so negative, borderline reject) didn’t assess the paper after the rebuttal, I recommend accepting the paper for poster presentation.

Reviews: review: paper_summary: paper_summary: The emphasis of this work is on complementing missing features through the cooperation on both the instance and feature levels among multiple views. This is an aspect that previous researchers did not touch upon, making it the innovative part of this work. Additionally, this article is rich in experiments and the theoretical proofs are also well-founded. However, there are still some issues with this paper.
strength_weakness: strength_weakness: 1.This paper complements missing features through the cooperation on both the instance and feature levels among multiple views, which is an angle that previous works have not touched upon.
2.This work addresses the shortcomings of existing multi-view clustering methods in handling missing attribute data and provides an effective solution for the problem of missing attribute multi-view clustering.
3.A new optimization method has been utilized to address this problem.
limitations: limitations: 1.At the feature level, the author used self-representation, which seems to be a common method. The method used in the Reliable Imputation Guidance Module part also directly applied an existing method, which does not reflect the author's sufficient innovation.
2.The number of views in the dataset is too small, just 2, which does not quite meet the related settings of multi-view, thus the persuasiveness of the experiment is somewhat lacking.
3.In the experimental part, the methods compared seem to have not compared the methods mentioned in the related work. The methods compared are all those not involving Instance-missing and Attribute-missing.
4.There is no detailed explanation as to why the Guiding matrix H is used. Can you provide specific literature references for this?
5.The RMKM and AMGL baseline methods are considered outdated.
paper_summary: paper_summary: The authors have developed an innovative Multi-View Clustering method that adeptly addresses the challenge of missing attributes. This represents a niche research direction, yet it is interesting. This model includes an adaptive dual-graph learning module specifically designed to preserve the underlying topological structure across varied feature spaces. Furthermore, the authors have developed an imputation guidance strategy that assesses the confidence of zero values, facilitating a precise approach to the imputation of missing data while effectively reducing the risk of over-interpolation. Empirical evidence from the analysis of six real-world datasets conclusively affirms the superior efficacy of the proposed RAM-MVC method.
strength_weakness: strength_weakness: - This paper significantly advances the field of attribute-missing multi-view clustering, offering insightful solutions that greatly enhance our understanding and capabilities in handling missing attributes.
- The paper introduces a novel concept that combines instance-level and feature-level graphs, clearly delineating the difference between technical and real zero values, thus enhancing the accuracy and interpretability of multi-view clustering methods.
- The paper features high-quality figures that are both informative and visually appealing. Additionally, the text is precise and clear, making the complex subject matter accessible.
limitations: limitations: -The dimensions of different datasets as presented in Table 2 appear to exhibit a degree of inconsistency, which prompts curiosity about the methodology the authors employed to ascertain the data dimensions across various views. The authors should publish a detailed approach to data preprocessing and feature selection.
-According to Fig. 5, the constructed temperature parameter t appears to have a relatively limited impact on the outcomes, suggesting that the model's sensitivity to this variable might be lower than anticipated, which warrants further investigation into its role in the algorithm's performance.
-The authors employ a biomedical multi-view dataset to assess the efficacy of their proposed model. However, the paper could be enhanced by providing a more thorough introduction to the dataset's background, detailing its composition, relevance, and the specific challenges it presents in the context of multi-view clustering.
-In Section 3.6, is it necessary to replace the symbol dv with dp? The author should check it.
- I am a little curious why the datasets involved in this article are almost two-views. Please explain it.
paper_summary: paper_summary: This study introduces a novel approach to the Multi-View Clustering model, RAM-MVC. The method addresses a significant problem in existing MVC methods, namely, the omission of missing attributes. The authors focus on the synergistic effects between the instance and feature spaces to avoid distorted imputation outcomes. Additionally, the authors propose differentiating between real and technical zeros to prevent over-imputation. This work is insightful, and the experiments rigorously demonstrate the effectiveness of the algorithm.
strength_weakness: strength_weakness: 1.The manuscript is excellently crafted, offering clear and direct explanations that facilitate easy understanding. The concepts presented are detailed, ensuring that they are communicated with precision and clarity.
2.The implementation of the reliable guidance module alongside the bi-level imputation module is notably impressive and highly effective. These components make a substantial contribution to the performance of the model.
3.The experimental section is extraordinarily thorough, encompassing detailed ablation studies that examine the contributions of different model components. Furthermore, the results not only meet but significantly exceed the current state-of-the-art benchmarks, demonstrating the model's superior performance and the effectiveness of the proposed methodology.
limitations: limitations: 1.The method for addressing incomplete samples is well-established in existing MVC and is no longer considered a novel technology. During the process of sample imputation, features are also reconstructed. Consequently, I am curious about the advantages of the attribute imputation method proposed by the author.
2. There are some unclear aspects in the description of the experimental setup, such as the generation of the attribute-missing dataset and the definitions of technical and true missing events. Why is the algorithm evaluated in a single-cell scenario? What do technical and true missing represent in single-cell data, and what is their real-world significance?
3.Differentiating between real and technical attributes that are missing in multiview scenarios is interesting, and it would be beneficial if the authors could add and discuss relevant related work.
4.There remains room for enhancement in certain details to improve the quality of the paper. For instance, there appear to be some typographical errors in the "Time Complexity Analysis" section. I would suggest correcting it.
5. Equations (8) and (4) appear to have similar structures. Are these calculations performed twice?
paper_summary: paper_summary: The paper introduces a novel algorithm, the Reliable Attribute-Missing Multi-View Clustering method (RAM-MVC), designed specifically to address missing attribute issues in multi-view data. The method focuses on distinguishing between true zeros and technical zeros during missing value imputation to avoid over-imputation. It combines a reliable imputation guidance module with dual-layer collaborative imputation to enhance feature reconstruction and clustering performance. Furthermore, experiments conducted on real-world single-cell multi-view datasets validate the effectiveness of the proposed method.
strength_weakness: strength_weakness: （1） the paper is well-structured；
（2）the methods and experimental results are easily understandable。
limitations: limitations: (1) While the authors provide a detailed explanation of "missing attributes" in the main text, it is advisable to briefly introduce this concept in the introduction for reader comprehension.

(2) Although the experiments evaluate the model in a biomedical scenario, demonstrating its effectiveness in this domain, further experimentation with datasets from other fields would elucidate RAM-MVC's performance across different domains and potential challenges, thus enhancing its generalizability.

(3) Figure 4, depicting similarity matrices corresponding to various variants in the ablation study, may not effectively convey the matrices for each experiment due to color choices. It is recommended that the authors use a different form of visualization, such as using contrasting colors with clear contrast, to enhance clarity.

(4) In the analysis of experimental results, the authors seem to lack specific analysis of the results related to runtime. Adding a section to analyze the reasons behind the observed runtime results would enhance the manuscript.