In recent years, symmetric non-negative matrix factorization (SNMF), a variant of non-
negative matrix factorization (NMF), has emerged as a promising tool for data analysis. This …

Several factors, including advances in computational algorithms, the availability of high-
performance computing hardware, and the assembly of large community-based databases …

The learning of image representation is always the most important problem in computer
vision community. In this paper, we propose a novel image representation method by …

Non-negative matrix factorization (NMF) has become one of the most powerful methods for
clustering and feature selection. However, the performance of the traditional NMF method …

Feature selection is an important approach for reducing the dimension of high-dimensional
data. In recent years, many feature selection algorithms have been proposed, but most of …

Microarray gene expression data gained great importance in recent years due to its role in
disease diagnoses and prognoses which help to choose the appropriate treatment plan for …

Nonnegative matrix factorization (NMF) has been a vital data representation technique, and
has demonstrated significant potential in the field of machine learning and data mining …

Hyperspectral unmixing is one of the crucial steps for many hyperspectral applications. The
problem of hyperspectral unmixing has proved to be a difficult task in unsupervised work …

In-situ analytics has lately been shown to be an effective approach to reduce both I/O and
storage costs for scientific analytics. Developing an efficient in-situ implementation, however …

Multi-view non-negative matrix factorization (NMF) has been developed to learn the latent
representation from multi-view non-negative data in recent years. To make the …