Data missing is a common issue in real-world applications, posing significant challenges for incomplete data processing. Traditional incomplete multi-view clustering methods rely on manually-designed optimization problems based on prior interpretable knowledge, considering the full utilization of available data. However, their limited feature extraction capability may become a bottleneck. In contrast, deep optimization methods leverage learning-based nonlinear transformations for clustering. They primarily achieve data imputation through the generalization ability of deep models, but their model interpretability may be limited by the black-box nature. Moreover, most existing methods only explore the structure of each view independently, where these structures are fixed and cannot form a complete unified structure. To address these issues, we propose a Structural Optimization-inspired Interpretable Network (SOI-Net) for incomplete multi-view clustering. Specifically, we project the features of all views into a unified representation space with the un-missing information of the views as constraints. By optimizing consistent structural information, we preserve the structures of missing modalities in the unified representation space, thereby mitigating the impact of missing data. Meanwhile, we derive network components based on the optimization problem to guide the learning of structure and representation. The practical significance of these network components provides model design-level interpretability. Extensive experiments on six datasets validate the effectiveness of SOI-Net in handling incomplete multi-view clustering task.

Text-Attributed Graphs (TAGs) are vital for modeling entity relationships across various domains. Graph Neural Networks have become cornerstone for processing graph structures, while the integration of text attributes remains a prominent research. The development of Large Language Models (LLMs) provides new opportunities for advancing textual encoding in TAGs. However, LLMs face challenges in specialized domains due to their limited task-specific knowledge, and fine-tuning them for specific tasks demands significant resources. To cope with the above challenges, we propose HiTuner, a novel framework that leverages fine-tuned Pre-trained Language Models (PLMs) with domain expertise as tuner to enhance the hierarchical LLM contextualized representations for modeling TAGs. Specifically, we first strategically select hierarchical hidden states of LLM to form a set of diverse and complementary descriptions as input for the sparse projection operator. Concurrently, a hybrid representation learning is developed to amalgamate the broad linguistic comprehension of LLMs with task-specific insights of the fine-tuned PLMs. Finally, HiTuner employs a confidence network to adaptively fuse the semantically-augmented representations. Empirical results across benchmark datasets spanning various domains validate the effectiveness of the proposed framework. Our codes are available at: https://github.com/ZihanFang11/HiTuner

Existing multi-view learning models struggle in open-set scenarios due to their implicit assumption of class completeness. Moreover, static view-induced biases, which arise from spurious view-label associations formed during training, further degrade their ability to recognize unknown categories. In this paper, we propose a multi-view open-set learning framework via ambiguity uncertainty calibration and view-wise debiasing. To simulate ambiguous samples, we design O-Mix, a novel synthesis strategy to generate virtual samples with calibrated open-set ambiguity uncertainty. These samples are further processed by an auxiliary ambiguity perception network that captures atypical patterns for improved open-set adaptation. Furthermore, we incorporate an HSIC-based contrastive debiasing module that enforces independence between view-specific ambiguous and view-consistent representations, encouraging the model to learn generalizable features. Extensive experiments on diverse multi-view benchmarks demonstrate that the proposed framework consistently enhances unknown-class recognition while preserving strong closed-set performance. The source code are available https://github.com/ZihanFang11/2025_MOCD_ACMMM

Facing the increasing heterogeneity of data in the real world, multi-view learning has become a crucial area of research. Graph Convolutional Networks (GCNs) are powerful for modeling both graph structures and features, making them a focal point in multi-view learning research. However, these methods typically only account for static data dependencies within each view separately when constructing the topology necessary for GCNs, overlooking potential relationships across views in multi-view data. Furthermore, there is a notable absence of theoretical guidance for constructing multi-view data topologies, leading to uncertainty regarding the progression of graph embeddings toward a consistent state. To tackle these challenges, we introduce a framework named energy-constrained multi-view graph diffusion. This approach establishes a mathematical correspondence between multi-view data and GCNs via graph diffusion. It treats multi-view data as a unified entity and devises a feature propagation process with inter-view awareness by considering both inter-view and intra-view feature flow across the entire system. Additionally, an energy function is introduced to guide the inter- and intra-view diffusion, ensuring that the representations converge towards global consistency. The empirical research on several benchmark datasets substantiates the benefits of the proposed method.

Existing multi-view graph learning methods often rely on consistent information for similar nodes within and across views, however they may lack adaptability when facing diversity challenges from noise, varied views, and complex data distributions. These challenges can be mainly categorized into: 1) View-specific diversity within intra-view from noise and incomplete information; 2) Cross-view diversity within inter-view caused by various latent semantics; 3) Cross-group diversity within inter-group due to data distribution differences. To this end, we propose a universal multi-view consensus graph learning framework that considers both original and generative graphs to balance consistency and diversity. Specifically, the proposed framework can be divided into the following four modules: i) Multi-channel graph module to extract principal node information, ensuring view-specific and cross-view consistency while mitigating view-specific and cross-view diversity within original graphs; ii) Generative module to produce cleaner and more realistic graphs, enriching graph structure while maintaining view-specific consistency and suppressing view-specific diversity; iii) Contrastive module to collaborate on generative semantics to facilitate cross-view consistency and reducing cross-view diversity within generative graphs; iv) Consensus graph module to consolidate learning a consensual graph, pursuing cross-group consistency and cross-group diversity. Extensive experimental results on real-world datasets demonstrate its effectiveness and superiority.

Existing representation learning approaches lie predominantly in designing models empirically without rigorous mathematical guidelines, neglecting interpretation in terms of modeling. In this work, we propose an optimization-derived representation learning network that embraces both interpretation and extensibility. To ensure interpretability at the design level, we adopt a transparent approach in customizing the representation learning network from an optimization perspective. This involves modularly stitching together components to meet specific requirements, enhancing flexibility and generality. Then, we convert the iterative solution of the convex optimization objective into the corresponding feed-forward network layers by embedding learnable modules. These above optimization-derived layers are seamlessly integrated into a deep neural network architecture, allowing for training in an end-to-end fashion. Furthermore, extra view-wise weights are introduced for multi-view learning to discriminate the contributions of representations from different views. The proposed method outperforms several advanced approaches on semi-supervised classification tasks, demonstrating its feasibility and effectiveness.