Image captured under poor-illumination conditions often display attributes of having poor
contrasts, low brightness, a narrow gray range, colour distortions and considerable …

Multi-view clustering, which exploits the multi-view information to partition data into their
clusters, has attracted intense attention. However, most existing methods directly learn a …

Unsupervised hashing methods have attracted widespread attention with the explosive
growth of large-scale data, which can greatly reduce storage and computation by learning …

With the development of web technology, multi-modal or multi-view data has surged as a
major stream for big data, where each modal/view encodes individual property of data …

Multi-view clustering is a long-standing important task, however, it remains challenging to
exploit valuable information from the complex multi-view data located in diverse high …

The prompt and accurate diagnosis of breast lesions, including the distinction between
cancer, non-cancer, and suspicious cancer, plays a crucial role in the prognosis of breast …

Abstract Multi-View Clustering (MVC) is an emerging research area aiming to cluster
multiple views of the same data, which has recently drawn substantial attention. Various …

Multi-view non-negative matrix factorization (NMF) provides a reliable method to analyze
multiple views of data for low-dimensional representation. A variety of multi-view learning …

Semantic segmentation is used in many fields, and most fields not only require models with
high-quality predictions but also require real-time speed in the forward inference phase …

Multi-view clustering (MVC) has been extensively studied to collect multiple source
information in recent years. One typical type of MVC methods is based on matrix …