Graph learning has emerged as a promising technique for multi-view clustering due to its
efficiency of learning a unified graph from multiple views. Previous multi-view graph learning …

Clustering aims to partition an input dataset into distinct groups according to some distance
or similarity measurements. One of the most widely used clustering method nowadays is the …

Real data are often collected from multiple channels or comprised of different
representations (ie, views). Multi-view learning provides an elegant way to analyze the multi …

Datasets are often collected from different resources or comprised of multiple
representations (ie, views). Multi-view clustering aims to analyze the multi-view data in an …

Clustering is an important topic in machine learning and data mining. Recently, deep
clustering, which learns feature representations for clustering tasks using deep neural …

Constructing the adjacency graph is fundamental to graph-based clustering. Graph learning
in kernel space has shown impressive performance on a number of benchmark data sets …

The development of Internet of Things (IoT) technologies has led to the widespread adoption
of monitoring networks for a wide variety of applications, such as smart cities, environmental …

Supervised deep learning techniques have achieved success in many computer vision
tasks. However, most deep learning methods are data hungry and rely on a large number of …

Graph non-negative matrix factorization (GNMF) can discover the data's intrinsic low-
dimensional structure embedded in the high-dimensional space. So, it has superior …

Multi-view learning aims to explore a global common structure shared by different views
collected from multiple individual sources. The nascent field of federated learning tries to …