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Despite significant advancements in multi-view multi-label learning driven by its broad applicability, real-world scenarios frequently suffer from dual incompleteness in both view and label spaces due to data acquisition uncertainties. The incompleteness of multi-view features degrades the comprehensiveness of sample representations, leading to failure in capturing semantically discriminative patterns essential for classification. To address the aforementioned challenges, we propose a novel learning framework termed V iew- L abel Driven C ross-Space S tructure A lignment Network (VLCSA). Departing from existing low-quality view completion approaches, we devise a view-label hybrid-driven autoencoder (VLAE) that extracts discriminative view-specific features through joint optimization of cross-view semantic consistency and label-guided instance-level embeddings, which allows for accurate reconstruction of missing views. Furthermore, we propose cross-space structure alignment (CSA), which imposes view-label hybrid-driven losses on both original and complete feature spaces to enforce structural consistency between partial and holistic semantic topologies. Recognizing the suboptimal data reconstruction quality during initial training phases, we propose phase-aware excitation (PAE) to mitigate error accumulation in early-stage learning. Additionally, we introduce star-shaped interactive sharing module (SIS) that facilitates efficient cross-view information exchange while leveraging view complementarity to ensure consistent and robust feature aggregation, circumventing conflicts with view-consistency alignment objectives. d on five widely-adopted benchmark datasets indicate that the proposed VLCSA framework outperforms numerous established baselines in terms of comprehensive evaluation metrics.

In practical application, the gathered multi-view data typically misses samples, known as incomplete multi-view data. Most existing incomplete multi-view clustering methods obtain consensus information in multi-view data by completing incomplete data using zero, mean values, etc. These approaches often ignore the higher-order relationship and structural information between different views. To alleviate the above problems, we propose enhanced tensor incomplete multi-view clustering with dual adaptive weight (ETIMC), which can acquire the higher-order relationship, and structural information between multiple perspectives, adaptively recover the missing samples and distinguish the contribution degree of different views. Specifically, the embedded representations obtained from incomplete multi-view data are stacked into a third-order tensor to capture the higher-order relationship. Then, a consensus matrix can be drawn from these potential representations via a self-weighting mechanism. Additionally, we adaptively reconstruct the missing samples while capturing structural information by the hypergraph Laplacian item. Moreover, we integrate the embedded representation of each view, tensor constraints, hypergraph Laplacian regularization, and dual adaptive weighted mechanisms into a unified framework. Experimental results on natural and synthetic incomplete datasets show the superiority of ETIMC.

Traditional graph-based multi-view learning methods usually assume that data are complete. Whereas several instances of some views may be missing, making the corresponding graphs incomplete and reducing the virtue of graph regularization. To mitigate the negative effect, a novel method, called incomplete multi-view learning via consensus graph completion (IMLCGC), is proposed in this paper, which completes the incomplete graphs based on the consensus among different views and then fuses the completed graphs into a common graph. Specifically, IMLCGC develops a learning framework for incomplete multi-view data, which contains three components, i.e., consensus low-dimensional representation, graph regularization, and consensus graph completion. Furthermore, a generalization error bound of the model is established based on Rademacher’s complexity. It shows the theory that learning with incomplete multi-view data is difficult. Experimental results on six well-known datasets indicate that IMLCGC significantly outperforms the state-of-the-art methods.

Multi-view multi-label classification (MvMLC) has garnered significant interest because of its ability to handle complex datasets. However, the inherent complexity of real-world data often results in incomplete views and missing labels, which limit the richness of data and hinder the accurate association of features with their corresponding categories. Additionally, the MvMLC task is intricate due to the need for diverse views to coherently represent the same entity, thus demanding the creation of stable and consistent multi-view representations that can ensure a reliable feature alignment process across heterogeneous perspectives. To address these challenges, we propose a model based on a Siamese network with squeeze attention (SSA) for incomplete multi-view multi-label classification (iMvMLC). Specifically, to capture the shared semantic information across different views, we combine cross-view collaborative synthesis (CCS) and viewwise representation calibration (VRC) mechanisms. CCS enhances the semantic interaction between views by introducing directive blocks and stacked autoencoders on top of the Siamese network, thereby improving the ability to extract shared semantic representations. The VRC mechanism uses contrastive learning with positive and negative sample pairs to refine the shared semantic space, ensuring higher feature consistency and better alignment across views. Furthermore, considering the task-specific importance variation exhibited by each view, we apply the squeeze attention-weighted fusion (SWF) strategy, which performs feature dimensionality reduction to amplify the key characteristics from each view and enables the model to flexibly adjust the influence of each perspective. Extensive evaluations conducted across five datasets demonstrate that the SSA method outperforms many existing approaches.

Incomplete multi-view clustering, which aims to divide different groups into incomplete views produced by various sensors, has attracted research attention. In this article, we propose a local structure learning for incomplete multi-view clustering (LS-IMC) algorithm. The algorithm jointly learns a consensus of incomplete views and a clustering result. Specifically, by fusing consistent representation and local structure learning into one optimization term, we can adequately capture the intrinsic geometric structure from missing and available data. In addition, the weight of incomplete views is learned adaptively to balance the importance of different views. Furthermore, we integrate representation learning and clustering processes into a unified framework so that the clustering result can be obtained directly and without the need for post-processing. Experiments performed on eight incomplete multi-view datasets demonstrate the effectiveness of the proposed LS-IMC compared to other current approaches.

Abstract Accurate lithology identification is often hindered by the limited use of multi-source information and the poor generalization capability of conventional methods, especially in geologically complex areas. Typically, the logging data in an area are multi-source and may be incomplete. In this study, a multi-view learning approach, which is named PDS-MVKNN and can effectively leverage multi-source logging data and accommodate incomplete datasets, is proposed for lithology identification. This approach incorporates the partial distance strategy, a method for calculating the distance between two feature vectors with missing values, into the multi-view K-nearest neighbours classifier and optimizes its view fusion strategy. In lithology identification on a dataset of complex igneous rocks, PDS-MVKNN achieves reliable performance even with a 10.43% data missing rate, reaching an identification accuracy of 93.80%. Compared with existing view fusion strategies, the model with the optimized strategy achieves a higher accuracy in lithology identification. By simulating different data missing rates in the dataset and various missing situations of logging curves in wells, this study also validates that PDS-MVKNN exhibits strong robustness to data missing.

With the enormous amount of multi-source data produced by various sensors and feature extraction approaches, multi-view clustering (MVC) has attracted developing research attention and is widely exploited in data analysis. Most of the existing multi-view clustering methods hold on the assumption that all of the views are complete. However, in many real scenarios, multi-view data are often incomplete for many reasons, e.g., hardware failure or incomplete data collection. In this paper, we propose an adaptive weighted graph fusion incomplete multi-view subspace clustering (AWGF-IMSC) method to solve the incomplete multi-view clustering problem. Firstly, to eliminate the noise existing in the original space, we transform complete original data into latent representations which contribute to better graph construction for each view. Then, we incorporate feature extraction and incomplete graph fusion into a unified framework, whereas two processes can negotiate with each other, serving for graph learning tasks. A sparse regularization is imposed on the complete graph to make it more robust to the view-inconsistency. Besides, the importance of different views is automatically learned, further guiding the construction of the complete graph. An effective iterative algorithm is proposed to solve the resulting optimization problem with convergence. Compared with the existing state-of-the-art methods, the experiment results on several real-world datasets demonstrate the effectiveness and advancement of our proposed method.

Multi-view clustering (MVC) is an essential and challenging task in machine learning and data mining. In recent years, this field has attracted a lot of attention and achieved remarkable results. The success of multi-view clustering relies heavily on the consistency and integrity of data views to ensure complete data information. In the process of data collection and transmission, data inevitably be lost, which leads to the occurrence of partial view unalignment (VN) and partial view missing (VM). This situation reduces the available information and increases the difficulty of joint learning of multi-view data. To address the incomplete information problem, in this article, we present a novel incomplete multi-view clustering via attention-based contrast learning framework (MCAC) to address the VN and VM puzzles. Due to the diversity of different views, negative samples are formed by randomly selecting some cross-view samples from positive samples, then computing the correlation between local features and latent features for each view by maximizing mutual information and, fusing each specific low-dimensional representation into a joint representation through an attention fusion layer, in addition, adding noise contrast loss reduces or even eliminates the effect of negative samples. MCAC conducts experiments on seven multi-view datasets and demonstrates the effectiveness compared to eleven state-of-the-art methods on the multi-view clustering task.

The goal of multi-view spectral clustering (MVSC) is to explore the intrinsic cluster structures embedded in the multi-view data and group the learned optimal feature embeddings into different clusters based on similarity measurement. Although encouraging improvements have been achieved, when facing the incomplete multi-view data, these MVSC methods would be disabled since most of them are generally built on the common assumption that data is required to have complete multiple descriptions, which obviously violates real-world situations. In this paper, we propose a novel Structured Anchor-inferred Graph Learning (SAGL) method to tackle the challenging universal incomplete multi-view spectral clustering problem, which can handle arbitrary view missing cases. Specifically, instead of using the fixed distance-based weighting matrix in the existing incomplete MVSC, we construct a structural anchor-based similarity learning model to formulate a learnable asymmetric intra-view similarity matrix. Meanwhile, the inter-view similarities can be successfully bridged by the paired anchor samples, which can skillfully overcome the limitation of insufficient information operations on incomplete multi-view data. Particularly, we further extend the two-view cases to the late fusion version of universal cases for accurate similarity calculation on incomplete multi-view data. Moreover, we derive a complete anchor-inferred graph learning scheme to enhance the efficiency of the spectral clustering process, which can well capture the hidden connection information among multi-view data, yielding improved clustering performance. Furthermore, we design a fast learning algorithm to solve the resulting optimization problem. Extensive experiments on multiple multi-view datasets show the superiority and advantages of the proposed method when handling different types of multi-view data with arbitrary information missing.

Multi-view clustering utilizes the consistency and complementarity between views to group entities well. However, in real life, the lack of instances in some views often occurs, which not only reduces the available information, but also increases the difficulty of joint learning with non-aligned multi-view data. Many incomplete multi-view clustering algorithms are developed to tackle these concerns, but they usually have the following problems: 1) They mainly focus on how to construct the shared feature space for incomplete views while ignoring the essential relationship between data instances. 2) Most of them simply assume that two datapoints which are close belong to the same category, but that is not the case. 3) The hazards of overlapping, confusing features in incomplete multi-view clustering are not considered. To solve these issues, this paper proposes a new Incomplete Multi-view Graph Learning method based on Weighted Sparse and Low rank Representation (IMGLWSLR). It leverages subspace learning with double constraints to capture global and local data relationships, a weighting mechanism to reduce the negative impact of missing data and a kernel-based method to fuse incomplete multiple views. Different from previous approaches, we concentrate on inhibiting the confusion of redundant features in subspace learning, which may affect the clustering seriously with missing views. Experimental results demonstrate the superiority of IMGLWSLR over nine benchmark datasets, compared with seven state-of-the-art approaches.