Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

In recent years, the rapid advances in machine learning (ML) and information fusion has
made it possible to endow machines/computers with the ability of emotion understanding …

Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …

Graph can achieve good performance to extract the low-dimensional features of
hyperspectral image (HSI). However, the present graph-based methods just consider the …

Hyperspectral images (HSIs) provide detailed spectral information through hundreds of
(narrow) spectral channels (also known as dimensionality or bands), which can be used to …

Deep learning has been shown to be successful in a number of domains, ranging from
acoustics, images, to natural language processing. However, applying deep learning to the …

In recent years, the appearance of the broad learning system (BLS) is poised to
revolutionize conventional artificial intelligence methods. It represents a step toward building …

Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

The industrial gearboxes usually work in harsh and variable conditions, which results in
partial failure of gears or bearings. Accordingly, the continuous irregular fluctuations of …

Graph is an important data representation which appears in a wide diversity of real-world
scenarios. Effective graph analytics provides users a deeper understanding of what is …