Considering the labeled samples may be difficult to obtain because they require human
annotators, special devices, or expensive and slow experiments. Semi-supervised learning …

A graph is one of important mathematical tools to describe ubiquitous relations. In the
classical graph theory and some applications, graphs are generally provided in advance, or …

Graph-based feature extraction is an efficient technique for data dimensionality reduction,
and it has gained intensive attention in various fields such as image processing, pattern …

Electroencephalogram (EEG) signals are generated from central nervous system which are
difficult to disguise, leading to its popularity in emotion recognition. Recently, semi …

Mining useful clusters from high dimensional data have received significant attention of the
computer vision and pattern recognition community in the recent years. Linear and nonlinear …

Semi-supervised learning is attracting increasing attention due to the fact that datasets of
many domains lack enough labeled data. Variational Auto-Encoder (VAE), in particular, has …

In the literature, most existing graph-based semi-supervised learning methods only use the
label information of observed samples in the label propagation stage, while ignoring such …

Constructing a good graph that can capture intrinsic data structures is critical for graph-
based semi-supervised learning methods, which are widely applied for hyperspectral image …

Due to the inefficiency of gradient-based iterative ways in network training, randomization-
based neural networks usually offer non-iterative closed form solutions. The random vector …

Dimensionality reduction is a challenging task for high-dimensional data processing in
machine learning and data mining. It can help to reduce computation time, save storage …