Multimodal data is typically collected through heterogeneous sensors and processing pipelines. However, due to variations in acquisition environments, device capabilities, and feature extraction methods, such data often suffers from incompleteness and inconsistent quality across modalities. To address these challenges, prior studies have explored modality selection and data completion strategies to improve information fusion. Nevertheless, these approaches face two main limitations: (1) they struggle to simultaneously ensure computational efficiency for large-scale graph data and maintain structural and semantic consistency across heterogeneous modality graphs; and (2) most of them operate at the modality level and fail to capture fine-grained, sample-specific quality variations. To overcome these issues, we propose a novel clustering framework, Sample Weighted Incomplete Multimodal Clustering Based on Graph Coarsening Label Extraction (IMC-GCSW). The proposed method introduces a graph coarsening-based label extraction strategy. It significantly reduces the computational cost of multimodal graph processing, while preserving key node information and local topological structures. Furthermore, a quality-aware sample weighting strategy is designed to enable fine-grained modeling of modality-specific data quality, allowing the model to dynamically suppress the influence of low-quality modalities on individual samples. Experiments on both general-purpose datasets and the Fructus Aurantii Disease and Pest Datasets demonstrate that the proposed method exhibits superior performance and strong adaptability in handling multimodal data with incompleteness and quality inconsistency.

In real world applications, multi-view data has attracted intensive attention due to the complex and complementary relationship across views. Multi-view representation learning (MvRL) focuses on obtaining consistent feature representation from multi-view data, and becomes a popular topic in multi-view research field. However, the relationship between different samples, i.e., the graph information, is usually ignored or excavated insufficiently in most existing MvRL methods, which only regard graph structure as regularization items instead of graph embedding for multi-view data. Besides, the limited learning capacity of the adopted shallow models is another challenge for MvRL. To tackle them, in this paper, we propose a novel unsupervised deep multi-view representation learning model guided by learnable graph structure, termed as LGG-DMRL. It first captures a multi-view consistent graph from original data based on self-representation learning, and explores the view-specific feature representation of each view by the designed graph guided attention network using the learnt graph. After that, the information bottleneck principle is employed to identify the shared representation across views integrated with the view-specific feature representations, promoting the multi-view complementarity and completeness. Experimental results on five real-world datasets demonstrate the superiority and effectiveness of our proposed LGG-DMRL compared with the recent state-of-the-art multi-view approaches.

Real-world data often contain incomplete views with varying degrees of missing information. While there are existing methods for learning representations from such data, effectively utilizing all incomplete view data and ensuring robustness to different levels of completeness remains a challenging task. To address this problem, we propose a novel framework named IMRL-AGI. IMRL-AGI combines the anchor graph-based Graph Convolutional Network (GCN) and information bottleneck. Specifically, the framework starts by constructing an anchor graph to effectively captures the nonlinear information between instances. Next, an anchor graph-based GCN is designed to extract feature information from various views. IMRL-AGI maximizes the mutual information between the views obtained by the common representation and the anchor-graph-based GCN, ensuring the accurate extraction of view information. Furthermore, the minimization of mutual information is applied to promote diversity and reduce redundancy in the multi-view representation. Extensive experiments are conducted on several real-world datasets, and the results demonstrate the superiority of IMRL-AGI.

Multi-view clustering (MVC) integrates information from different views to improve clustering performance compared to single-view clustering. However, the raw multi-view data in the feature space often contains irrelevant information to the clustering task, which is difficult to separate using existing methods. This irrelevant information is processed equally with clustering information, negatively impacting the final clustering performance. In this paper, we propose a new framework for multi-view clustering information reinforcement network (MVCIR-net) to alleviate these problems. Our method gives practical clustering meaning to the clustering distribution layer by contrastive learning. Then, the trusted neighbor instances distribution of the normalized graph is debias aggregated to form the clustering information propensity distribution, and the clustering information distribution is made to fit this distribution. In addition, the coupling degree of the clustering information distribution in different views on the same sample should be enhanced. Through the aforementioned strategies, the raw data is fuzzy mapped into clustering information, and the network's ability to recognize clustering information is strengthened. Finally, the fuzzy mapping data is input into the network and reconstructed to evaluate the quality of the extracted clustering information. Extensive experiments on public multi-view datasets show that MVCIR-net achieves superior clustering effectiveness and the ability to identify clustering information.

Multi-view clustering can cluster signal samples from multiple views into groups. Currently, multi-view clustering fuses the information of different views into a low-dimensional space for clustering. However, the direct reduction of high-dimensional information to a very low-dimensional space leads to the loss of a lot of sample semantic information, while a higher dimension after dimensionality reduction may blur the clustering structure of samples. To tackle these problems, we propose a novel framework called Mining Multi-view Clustering Space with Interpretable Space Search Constraint to explore the clustering structure while preserving the low-dimensional space semantic information. Our method maps samples from the raw space to a low-dimensional space separated into consensus and private features. This allows us to explore the interpretability of samples in the low-dimensional space to achieve representative representations. After assembling these representations, guided fusion is carried out and a search constraint is imposed to achieve a more reasonable clustering structure. Finally, by dynamically screening positive and negative samples, the clustering performance of the clustering space is maximized by contrastive learning. Extensive experiments on public datasets demonstrate that our method achieves state-of-the-art clustering effectiveness.