Weiqing Yan

Instance-Aligned Semantic Reconstruction for Incomplete Multi-View Clustering

Incomplete multi-view clustering (IMVC) aims to exploit complementary information from multiple views with missing observations. Recent diffusion-based approaches have shown promise for view completion; however, they often fail to capture instance-aligned global correlations across views and suffer from inefficient inference and loosely coupled optimization. In this paper, we propose IASR, an Instance-Aligned Semantic Reconstruction framework for IMVC. IASR formulates missing-view recovery as a cross-view token alignment generator, in which noisy targets, observed views, and timestep embeddings are jointly represented as tokens and interact to capture long-range cross-view dependencies throughout the denoising trajectory. To stabilize representation learning under generative noise, we further introduce a stable–active dual-encoder representation architecture with a generative-adaptive contrastive learning strategy that tightly couples view completion and clustering. Extensive experiments on eight benchmark datasets demonstrate that IASR consistently outperforms state-of-the-art IMVC methods, especially under high missing-rate settings, while achieving improved inference efficiency.

Hybrid Conditional Diffusion With Implicit Repair for Incomplete Multiview Clustering.

Multiview data often suffers from missing views, posing significant challenges for incomplete multiview clustering (IMVC). Existing methods typically perform explicit similarity feature-level imputation using observed views, yet they often fail to capture the shared latent distribution across different views, particularly under high missing rates. In addition, many IMVC models adopt a single-stage framework that jointly optimizes data recovery and clustering, resulting in increased model complexity and computational burden due to full-network backpropagation, thus limiting scalability and training efficiency. To address these issues, we propose a two-stage framework, termed hybrid conditional diffusion with implicit repair for IMVC (HCDIR-IMVC), which simplifies the integration of diffusion models while improving the consistency and robustness of the recovered features. In the first stage, we introduce a unified hybrid conditional diffusion restoration (HC-DR) model that leverages probabilistic learning to dynamically select observed views as conditional inputs for the diffusion process. This enables the model to effectively learn the distribution of missing views and guide their generation. To further enhance cross-view consistency, we incorporate contrastive learning into the diffusion framework. Unlike prior approaches that use separate diffusion models for each view, our method adopts a single hybrid-conditioned model, significantly reducing complexity and improving multiview feature utilization. In the clustering stage, we freeze the imputation model's parameters and remove the decoder to reduce training overhead. A representation and distribution alignment strategy is employed to ensure consistent clustering labels within and across views. Extensive experiments demonstrate that HCDIR-IMVC achieves superior performance in accuracy and computational efficiency, particularly under high missing rates.

Spot-Adaptive Structural Rectification for Spatially Resolved Transcriptomics Data Clustering