Non-negative matrix factorization (NMF) can map high-dimensional data into a low-
dimensional data space. Feature selection can eliminate the redundant and irrelevant …

Existing nonnegative matrix factorization methods usually focus on learning global structure
of the data to construct basis and coefficient matrices, which ignores the local structure that …

In recent years, a variety of feature selection algorithms based on subspace learning have
been proposed. However, such methods typically do not exploit information about the …

Graph-based clustering is one of the major clustering methods. Most of it works in three
separate steps: 1) similarity graph construction; 2) clustering label relaxing; and 3) label …

Spectral Clustering (SC) has been the main subject of intensive research due to its
remarkable clustering performance. Despite its successes, most existing SC methods suffer …

The past decade has witnessed the rapid development of deep learning techniques,
especially for large-scale and complex datasets. However, it is still a noteworthy problem in …

Previous graph-based semisupervised learning (G-SSL) methods have the following
drawbacks: 1) they usually predefine the graph structure and then use it to perform label …

Based on spectral graph theory, spectral clustering is an optimal graph partition problem. It
has been proven that the spectral clustering is equivalent to nonnegative matrix factorization …

Network analysis of human brain connectivity is critically important for understanding brain
function and disease states. Embedding a brain network as a whole graph instance into a …

Currently, unsupervised hyperspectral image (HSI) segmentation methods are mainly
implemented by clustering. Nevertheless, hyperspectral data contain a large amount of …