The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …

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

Abstract Semi-Supervised Graph Clustering (SSGC) has emerged as a pivotal field at the
intersection of graph clustering and semi-supervised learning (SSL), offering innovative …

Graph construction plays an essential role in graph-based label propagation since graphs
give some information on the structure of the data manifold. While most graph construction …

Graph carries out a key role in graph-based semi-supervised label propagation, as it
clarifies the structure of the data manifold. The performance of label propagation methods …

Graph learning (GL) can dynamically capture the distribution structure (graph structure) of
data based on graph convolutional networks (GCN), and the learning quality of the graph …

Facial Beauty Prediction (FBP) is an important visual recognition problem to evaluate the
attractiveness of faces according to human perception. Most existing FBP methods are …

Estimating the attractiveness of faces in images and videos is a relatively new problem in
computer vision. So far, supervised learning paradigms with deep or shallow models have …

Recently, graph-based semi-supervised learning (GSSL) has received much attention. On
the other hand, less attention has been paid to the problem of large-scale GSSL for inductive …

Graph-based semi-supervised learning (GSSL) has received much attention recently.
Despite some progress made in this area by some recent methods, some limitations still …