Nonnegative Matrix Factorization (NMF), a relatively novel paradigm for dimensionality
reduction, has been in the ascendant since its inception. It incorporates the nonnegativity …

As a popular dimensionality reduction method, nonnegative matrix factorization (NMF) has
been widely used in image classification. However, the NMF does not consider discriminant …

Non-negative matrix factorization (NMF) has recently attracted much attention due to its
good interpretation in perception science and widely applications in various fields. In this …

Multilayer network is a structure commonly used to describe and model the complex
interaction between sets of entities/nodes. A three-layer example is the author-paper-word …

In this paper, we propose a novel unsupervised feature selection method: Simultaneous
Orthogonal basis Clustering Feature Selection (SOCFS). To perform feature selection on …

Graph-regularized non-negative matrix factorization (GNMF) is proved to be effective for the
clustering of nonlinear separable data. Existing GNMF variants commonly improve model …

Low rank is an important but ill-posed problem in the development of nonnegative matrix
factorization (NMF) algorithms because the essential information is often encoded in a low …

Non-negative matrix factorisation (NMF) is a promising data-mining technique for non-
negative data. NMF achieves feature extraction by factorising the original data matrix into a …

Community detection is an important and challenging task in complex attribute network
analysis. Symmetric non-negative matrix factorization-based methods have become …

This paper introduces a Feature Nonlinear Transformation Non-Negative Matrix
Factorization with Kullback-Leibler Divergence (FNTNMF-KLD) for extracting the nonlinear …