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

Principal components analysis (PCA) is a well-known technique for approximating a tabular
data set by a low rank matrix. Here, we extend the idea of PCA to handle arbitrary data sets …

We review algorithms developed for nonnegative matrix factorization (NMF) and
nonnegative tensor factorization (NTF) from a unified view based on the block coordinate …

Non-negative Matrix Factorization (NMF), a classical method for dimensionality reduction,
has been applied in many fields. It is based on the idea that negative numbers are physically …

Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of
high-dimensional data as it automatically extracts sparse and meaningful features from a set …

Identifying the underlying structure of a data set and extracting meaningful information is a
key problem in data analysis. Simple and powerful methods to achieve this goal are linear …

Nonnegative matrix factorization (NMF) is a data analysis technique used in a great variety
of applications such as text mining, image processing, hyperspectral data analysis …

Approximate matrix factorization techniques with both nonnegativity and orthogonality
constraints, referred to as orthogonal nonnegative matrix factorization (ONMF), have been …

Nonnegative matrix factorization (NMF) has become a very popular technique in machine
learning because it automatically extracts meaningful features through a sparse and part …

In this paper, we design a fast hierarchical clustering algorithm for high-resolution
hyperspectral images (HSI). At the core of the algorithm, a new rank-two nonnegative matrix …