The field of multi-view learning and imputation is rapidly advancing, with a focus on developing innovative methods to handle incomplete and noisy data. Recent research has emphasized the importance of robust representation learning, adaptive imputation, and cross-view relationships. Notable trends include the use of graph neural networks, attention mechanisms, and contrastive learning to improve clustering and feature selection performance. Additionally, there is a growing interest in developing methods that can handle heterogeneous data, mixed-missing scenarios, and multi-source noise.

Noteworthy papers in this area include: Dynamic Deep Graph Learning for Incomplete Multi-View Clustering with Masked Graph Reconstruction Loss, which proposes a novel approach to construct a missing-robust global graph and leverages graph structure contrastive learning to improve clustering performance. PI-NAIM: Path-Integrated Neural Adaptive Imputation Model, which introduces a dual-path architecture that dynamically routes samples to optimized imputation approaches based on missingness complexity. RAC-DMVC: Reliability-Aware Contrastive Deep Multi-View Clustering under Multi-Source Noise, which constructs a reliability graph to guide robust representation learning under noisy environments. MissHDD: Hybrid Deterministic Diffusion for Heterogeneous Incomplete Data Imputation, which proposes a hybrid deterministic diffusion framework that separates heterogeneous features into two complementary generative channels.