A Novel Low-Rank Embedded Latent Multi-View Subspace Clustering Approach.

Although multi-view learning has made significant progress over the past few decades, it is still challenging due to the difficulty in modeling complex correlations among different views, especially under the context of view missing. To address the challenge, we propose a novel framework termed Cross Partial Multi-View Networks (CPM-Nets), which aims to fully and flexibly take advantage of multiple partial views. We first provide a formal definition of completeness and versatility for multi-view representation and then theoretically prove the versatility of the learned latent representations. For completeness, the task of learning latent multi-view representation is specifically translated to a degradation process by mimicking data transmission, such that the optimal tradeoff between consistency and complementarity across different views can be implicitly achieved. Equipped with adversarial strategy, our model stably imputes missing views, encoding information from all views for each sample to be encoded into latent representation to further enhance the completeness. Furthermore, a nonparametric classification loss is introduced to produce structured representations and prevent overfitting, which endows the algorithm with promising generalization under view-missing cases. Extensive experimental results validate the effectiveness of our algorithm over existing state of the arts for classification, representation learning and data imputation.

Deep multi-view representation learning focuses on training a unified low-dimensional representation for data with multiple sources or modalities. With the rapidly growing attention of graph neural networks, more and more researchers have introduced various graph models into multi-view learning. Although considerable achievements have been made, most existing methods usually propagate information in a single view and fuse multiple information only from the perspective of attributes or relationships. To solve the aforementioned problems, we propose an efficient model termed Dual Fusion-Propagation Graph Neural Network (DFP-GNN) and apply it to deep multi-view clustering tasks. The proposed method is designed with three submodules and has the following merits: a) The proposed view-specific and cross-view propagation modules can capture the consistency and complementarity information among multiple views; b) The designed fusion module performs multi-view information fusion with the attributes of nodes and the relationships among them simultaneously. Experiments on popular databases show that DFP-GNN achieves significant results compared with several state-of-the-art algorithms.

Significant efforts have been focused on enhancing the utilization of multiple node features and topological structures in multi-view graph learning through explicit model-driven and implicit deep learning-based methodologies. The former excels in embedding prior knowledge, thereby offering theoretical interpretability but is limited in application flexibility due to manual parameter selection. In contrast, the latter leverages automatic differentiation, providing greater flexibility but lacking theoretical interpretability due to their opaque nature. Motivated by these observations, we propose an interpretable deep unfolding network for mutual-benefit multi-view graph learning, aiming to combine the strengths of both approaches. Specifically, we employ the Alternating Direction Method of Multipliers (ADMM) to solve a multi-view graph learning model with sparse and low-rank constraints. This solution is then integrated into deep unfolding networks to enhance interpretability. Furthermore, we convert optimization conditions into implicit losses and utilize automatic differentiation to update parameters, reducing the need for manual tuning and increasing flexibility. This integration optimizes multi-view learning for a graph representation that balances interpretability and flexibility. Empirical evaluations on six diverse datasets demonstrate the effectiveness and superiority of the proposed method over state-of-the-art approaches.

Semantically consistent multi-view representation learning

Recent researches on multiview learning have received widespread attention due to the increasing generalization of multiview data. As an effective probabilistic model, random walk has also shown encouraging performance in various fields. To further exploit the potential of utilizing random walk schemes to address multiview learning problems, this article proposes a simple yet efficient multigraph random walk scheme for both multiview clustering and semisupervised classification tasks. The proposed model integrates random walk with multiview learning, and recursively learns a globally stable probability distribution matrix from multiple views, on the basis of which the label indicator is obtained in the scene of clustering or semisupervised classification. Furthermore, an adaptive weight vector is learned to incorporate the diversity and complementarity of multiview data. Besides, the relationships between the proposed scheme and spectral clustering, neighborhood embedding and manifold embedding are analyzed theoretically. Finally, comprehensive comparative experiments are conducted with several state-of-the-art multiview clustering and semisupervised classification methods on eight real-world datasets. The experimental results demonstrate the superiority of the proposed method in terms of both clustering and classification performance.

Cross-domain recommendation (CDR), which leverages information collected from other domains, has been empirically demonstrated to effectively alleviate data sparsity and cold-start problems encountered in traditional recommendation systems. However, current CDR methods, including those considering time information, do not jointly model the general and current interests within and across domains, which is pivotal for accurately predicting users’ future interactions. In this article, we propose a Contrastive learning-enhanced Multi-View interest learning model (CMVCDR) for cross-domain sequential recommendation. Specifically, we design a static view and a sequential view to model uses’ general interests and current interests, respectively. We divide a user’s general interest representation into a domain-invariant part and a domain-specific part. A cross-domain contrastive learning objective is introduced to impose constraints for optimizing these representations. In the sequential view, we first devise an attention mechanism guided by users’ domain-invariant interest representations to distill cross-domain knowledge pertaining to domain-invariant factors while reducing noise from irrelevant factors. We further design a domain-specific interest-guided temporal information aggregation mechanism to generate users’ current interest representations. Extensive experiments demonstrate the effectiveness of our proposed model compared with state-of-the-art methods.

Background and objectivePositron emission tomography/computed tomography (PET/CT) is recommended as the standard imaging modality for diffuse large B-cell lymphoma (DLBCL) staging. However, many studies have neglected the role of patients' prognostic factors with respect to imaging PET/CT of quantitative features. In this paper, a multi-view learning (MVL) model is established to make full use of both clinical and imaging data to predict the prognosis of DLBCL patients and thereby assist doctors in decision-making.MethodsFeature engineering, including feature extraction, feature screening by recursive feature elimination, and dimensionality reduction by principal component analysis, are successively performed on the clinical data and imaging data of the research subjects to obtain the study data. After dividing the data into training and test sets, an instance weighting method is applied to the training data. Subsequently, kernel mapping is performed on the imaging features and clinical features separately, and this kernel mapping is processed in the new kernel feature space using kernel canonical correlation analysis (KCCA). Lastly, model training is performed on the obtained common kernel subspace using a support vector machine (SVM). The final overall model, named SVM-2view-KCCA (SVM-2 K), was compared with three other multi-view models (Ensemble-SVM, Multi-view maximum entropy discrimination, and canonical correlation analysis). The performance of the model was evaluated on the test data with respect to several dichotomous metrics: accuracy, sensitivity, F1 score, the area under the curve (AUC), and G-mean.ResultsThe SVM model improved AUC by 10.5%, sensitivity by 11.9%, accuracy by 9.8%, F1 score by 9.2%, and G-mean by 7.8% for the DLBCL test data after feature engineering based on dimensionality reduction and instance weighting. In the performance comparison of single-view learning models, the SVM-based integration of clinical and imaging features achieved the best overall performance (AUC = 86.3%, accuracy = 91.6%, sensitivity = 83.2%, F1 = 85.7%, and G-mean = 86.1%). In the comparison of MVL models, SVM-2 K achieved the best overall performance (AUC = 92.1%, accuracy = 96.9%, sensitivity = 90.9%, F1 = 92.8%, and G-mean = 91.4%), and the performance of each MVL model was better than that of the best single-view learning model.ConclusionsMVL models outperformed single-view learning models. Of the MVL models, the proposed SVM-2 K achieved the best overall performance and could accurately predict patient prognosis.

Multi-view learning methods often focus on improving decision accuracy while neglecting the decision uncertainty, which significantly restricts their applications in safety-critical scenarios. To address this, trusted multi-view learning methods estimate prediction uncertainties by learning class distributions from each instance. However, these methods heavily rely on high-quality ground-truth labels. This motivates us to delve into a new problem: how to develop a reliable multi-view learning model under the guidance of noisy labels? We propose the Trusted Multi-view Noise Refining (TMNR) method to address this challenge by modeling label noise arising from low-quality data features and easily-confused classes. TMNR employs evidential deep neural networks to construct view-specific opinions that capture both beliefs and uncertainty. These opinions are then transformed through noise correlation matrices to align with the noisy supervision, where matrix elements are constrained by sample uncertainty to reflect label reliability. Furthermore, considering the challenge of jointly optimizing the evidence network and noise correlation matrices under noisy supervision, we further propose Trusted Multi-view Noise Re-Refining (TMNR$^{\mathbf{2}}$), which disentangles this complex co-training problem by establishing different training objectives for distinct modules. TMNR$^{\mathbf{2}}$ identifies potentially mislabeled samples through evidence-label consistency and generates pseudo-labels from neighboring information. By assigning clean samples to optimize evidential networks and noisy samples to guide noise correlation matrices, respectively, TMNR$^{\mathbf{2}}$ reduces mapping interference and achieves stabilized training. We empirically evaluate our methods against state-of-the-art baselines on 7 multi-view datasets. Experimental results demonstrate that TMNR$^{\mathbf{2}}$ significantly outperforms baseline methods, with average accuracy improvements of 7% on datasets with 50% label noise. The code and appendix are released at https://github.com/YilinZhang107/TMNRR.

Advances in single-cell biotechnologies have generated the single-cell RNA sequencing (scRNA-seq) of gene expression profiles at cell levels, providing an opportunity to study cellular distribution. Although significant efforts developed in their analysis, many problems remain in studying cell types distribution because of the heterogeneity, high dimensionality, and noise of scRNA-seq. In this study, a multi-view clustering with graph learning algorithm (MCGL) for scRNA-seq data is proposed, which consists of multi-view learning, graph learning, and cell type clustering. In order to avoid a single feature space of scRNA-seq being inadequate to comprehensively characterize the functions of cells, MCGL constructs the multiple feature spaces and utilizes multi-view learning to comprehensively characterize scRNA-seq data from different perspectives. MCGL adaptively learns the similarity graphs of cells that overcome the dependence on fixed similarity, transforming scRNA-seq analysis into the analysis of multi-view clustering. MCGL decomposes the networks of cells into view-specific and common networks in multi-view learning, which better characterizes the topological relationship of cells. MCGL simultaneously utilizes multiple types of cell-cell networks and fully exploits the connection relationship between cells through the complementarity between networks to improve clustering performance. The graph learning, graph factorization, and cell-type clustering processes are accomplished simultaneously under one optimization framework. The performance of the MCGL algorithm is validated with ten scRNA-seq datasets from different scales, and experimental results imply that the proposed algorithm significantly outperforms fourteen state-of-the-art scRNA-seq algorithms.

Background Pulmonary hypertension (PH) in newborns is a severe condition characterized by elevated pulmonary artery pressure, leading to right ventricular strain and impaired oxygenation, and significant morbidity and mortality. Right heart catheterization (RHC) is the gold standard for PH diagnosis but is invasive, costly, and associated with procedural risks. Instead, transthoracic echocardiography (ECHO) is widely used for non-invasive PH assessment, although its interpretation is highly operator-dependent. While automated machine learning methods have been proposed for PH detection, existing approaches largely focus on adults and often rely on single-view echocardiographic frames, limiting their generalizability to neonatal populations. Objective We aim to leverage a multi-view variational autoencoder (VAE) framework to predict PH severity in newborns using echocardiographic videos. We hypothesize that learning latent representations across multiple echocardiographic views improves classification performance and generalization compared to traditional models. Methods We analyzed a dataset of 936 transthoracic ECHO videos from 192 newborns (56±160 days old, 2.9±1.5Kg), with a held-out test set of 375 videos from 78 newborns (34±42 days old, 2.1±1.2Kg). Standard ECHO views included PLAX, A4C, and three parasternal short-axis views (PSAX-P, PSAX-S, PSAX-A). PH severity was categorized as none, mild, or moderate-to-severe based on septal flattening patterns. A multi-view VAE framework with a variational mixture-of-experts prior was trained to extract latent representations from ECHO videos, leveraging shared information across different views. Classification performance was compared against single-view and supervised learning from Ragnarsdottir et al. 24’ [1]. The study and method pipeline is illustrated in figure 1. Results Multi-view learning significantly improved PH classification accuracy compared to single-view approaches. Our VAE model achieved an AUROC of 0.83 ± 0.02 and a balanced accuracy of 0.74 ± 0.02 for binary PH detection, outperforming single-view VAEs and traditional supervised learning models. Importantly, our VAE exhibited greater robustness to unseen test data, demonstrating improved generalization across newborn populations. The model effectively distinguished PH severity levels, with the highest performance gains observed in the multi-class classification task (AUROC = 0.74 ± 0.01) –comprehensive results in figure 2. Conclusion Automated PH assessment using echocardiography is essential for early detection and intervention, particularly in neonates where timely diagnosis can impact survival. Our multi-view VAE-based framework enhances PH classification, reduces operator dependency, and improves diagnostic consistency. These improvements could enable more standardized and accessible screening, particularly in resource-limited settings where expert evaluation may not always be available.Study pipeline and method. Summary results (baseline [1]).

Self-taught Multi-view Spectral Clustering

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2016 SIAM International Conference on Data Mining (SDM)Robust Unsupervised Feature Selection on Networked DataJundong Li, Xia Hu, Liang Wu, and Huan LiuJundong Li, Xia Hu, Liang Wu, and Huan Liupp.387 - 395Chapter DOI:https://doi.org/10.1137/1.9781611974348.44PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Feature selection has shown its effectiveness to prepare high-dimensional data for many data mining and machine learning tasks. Traditional feature selection algorithms are mainly based on the assumption that data instances are independent and identically distributed. However, this assumption is invalid in networked data since instances are not only associated with high dimensional features but also inherently interconnected with each other. In addition, obtaining label information for networked data is time consuming and labor intensive. Without label information to direct feature selection, it is difficult to assess the feature relevance. In contrast to the scarce label information, link information in networks are abundant and could help select relevant features. However, most networked data has a lot of noisy links, resulting in the feature selection algorithms to be less effective. To address the above mentioned issues, we propose a robust unsupervised feature selection framework NetFS for networked data, which embeds the latent representation learning into feature selection. Therefore, content information is able to help mitigate the negative effects from noisy links in learning latent representations, while good latent representations in turn can contribute to extract more meaningful features. In other words, both phases could cooperate and boost each other. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. Previous chapter Next chapter RelatedDetails Published:2016eISBN:978-1-61197-434-8 https://doi.org/10.1137/1.9781611974348Book Series Name:ProceedingsBook Code:PRDT16Book Pages:1-867

Posterior collapse is a pervasive issue in Variational Autoencoders (VAEs) that leads to the learned latent representations becoming trivial and devoid of meaningful information. To address this problem, this paper presents a novel β-VAE approach, which incorporates a hyperparameter β to strike an optimal balance between the reconstruction loss and the KL divergence loss. By conducting a comprehensive series of experiments and drawing comparisons with existing methods, robust evidence is provided that the proposed β-VAE method effectively mitigates posterior collapse and yields more expressive and informative latent representations.The experimental setup involves various architectures and datasets to demonstrate the versatility and efficacy of the β-VAE approach in diverse settings. Additionally, ablation studies are performed to investigate the impact of different β values on the model's performance, elucidating the role of this hyperparameter in controlling the trade-off between reconstruction quality and latent representation expressiveness. Furthermore, the disentanglement properties of the learned latent space are analyzed, which is a crucial aspect of VAEs, especially when applied to complex, real-world data.In-depth analysis of the results offers valuable insights into the underlying mechanisms of β-VAE, thereby contributing to a more profound understanding of VAEs and their inherent limitations. The findings not only establish the effectiveness of the β-VAE method in preventing posterior collapse but also pave the way for future research on improving VAEs' performance in various applications. Potential future work could explore alternative techniques for balancing the competing objectives of reconstruction and latent representation learning or delve into the theoretical properties of β-VAE, providing a more rigorous foundation for this approach.

Driven by multi-omics data, some multi-view clustering algorithms have been successfully applied to cancer subtypes prediction, aiming to identify subtypes with biometric differences in the same cancer, thereby improving the clinical prognosis of patients and designing personalized treatment plan. Due to the fact that the number of patients in omics data is much smaller than the number of genes, multi-view spectral clustering based on similarity learning has been widely developed. However, these algorithms still suffer some problems, such as over-reliance on the quality of pre-defined similarity matrices for clustering results, inability to reasonably handle noise and redundant information in high-dimensional omics data, ignoring complementary information between omics data, etc. This paper proposes multi-view spectral clustering with latent representation learning (MSCLRL) method to alleviate the above problems. First, MSCLRL generates a corresponding low-dimensional latent representation for each omics data, which can effectively retain the unique information of each omics and improve the robustness and accuracy of the similarity matrix. Second, the obtained latent representations are assigned appropriate weights by MSCLRL, and global similarity learning is performed to generate an integrated similarity matrix. Third, the integrated similarity matrix is used to feed back and update the low-dimensional representation of each omics. Finally, the final integrated similarity matrix is used for clustering. In 10 benchmark multi-omics datasets and 2 separate cancer case studies, the experiments confirmed that the proposed method obtained statistically and biologically meaningful cancer subtypes.

Surface defect identification is a crucial task in many manufacturing systems, including automotive, aircraft, steel rolling, and precast concrete. Although image-based surface defect identification methods have been proposed, these methods usually have two limitations: images may lose partial information, such as depths of surface defects, and their precision is vulnerable to many factors, such as the inspection angle, light, color, noise, etc. Given that a three-dimensional (3D) point cloud can precisely represent the multidimensional structure of surface defects, we aim to detect and classify surface defects using a 3D point cloud. This has two major challenges: (i) the defects are often sparsely distributed over the surface, which makes their features prone to be hidden by the normal surface and (ii) different permutations and transformations of 3D point cloud may represent the same surface, so the proposed model needs to be permutation and transformation invariant. In this paper, a two-step surface defect identification approach is developed to investigate the defects’ patterns in 3D point cloud data. The proposed approach consists of an unsupervised method for defect detection and a multi-view deep learning model for defect classification, which can keep track of the features from both defective and non-defective regions. We prove that the proposed approach is invariant to different permutations and transformations. Two case studies are conducted for defect identification on the surfaces of synthetic aircraft fuselage and the real precast concrete specimen, respectively. The results show that our approach receives the best defect detection and classification accuracy compared with other benchmark methods.