Hyperspectral unmixing has been an important technique that estimates a set of
endmembers and their corresponding abundances from a hyperspectral image (HSI) …

This paper presents a comprehensive survey of the meta-heuristic optimization algorithms
on the text clustering applications and highlights its main procedures. These Artificial …

Feature selection is one of the most efficient procedures for reducing the dimensionality of
high-dimensional data by choosing a practical subset of features. Since labeled samples are …

Community detection is a popular yet thorny issue in social network analysis. A symmetric
and nonnegative matrix factorization (SNMF) model based on a nonnegative multiplicative …

Semi-supervised nonnegative matrix factorization exploits the strengths of matrix
factorization in successfully learning part-based representation and is also able to achieve …

Multi-view clustering has received widespread attention owing to its effectiveness by
integrating multi-view data appropriately, but traditional algorithms have limited applicability …

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 …

Multi-view clustering based on non-negative matrix factorization (NMFMvC) is a well-known
method for handling high-dimensional multi-view data. To satisfy the non-negativity …

As a variant of non-negative matrix factorization (NMF), symmetric NMF (SymNMF) can
generate the clustering result without additional post-processing, by decomposing a …

Most patients with cancer are refractory to immune checkpoint blockade (ICB) therapy, and
proper patient stratification remains an open question. Primary patient data suffer from high …