Multi-view clustering aims to extract valuable information from different sources or
perspectives. Over the years, the deep neural network has demonstrated its superior …

In recent years, significant progress has been made in Multi-view Subspace Clustering
(MSC). Most existing MSC methods attempt to explore and exploit the view correlations of …

Cloud–edge–unmanned aerial vehicle (UAV) computing collaboration is crucial for smart
and real-time applications. It highlights the challenges faced by UAVs in task execution …

Multi-view unsupervised feature selection has gained significant attention in effectively
reducing the dimensionality of unlabeled data collected from multiple sources. Many existing …

Unlike traditional supervised classification, complementary label learning (CLL) operates
under a weak supervision framework, where each sample is annotated by excluding several …

Spatial transcriptomics technology fully leverages spatial location and gene expression
information for spatial clustering tasks. However, existing spatial clustering methods …

With the development of information technology, a large number of multiview data has
emerged, which makes multiview clustering algorithms considerably attractive. Previous …

In smart grid services, electricity consumption data sharing may lead to privacy breaches
and the potential for misuse of personal data. Existing data aggregation approaches mainly …

Graph neural networks (GNNs) have shown great potential in representation learning for
various graph tasks. However, the distribution shift between the training and test sets poses …

Biomedical research increasingly relies on the analysis of complex interactions between
biological entities, such as genes, proteins, and drugs. Although advancements in …