Although nonnegative matrix factorization (NMF) is widely used, some matrix factorization
methods result in misleading results and waste of computing resources due to lack of timely …

In recent years, low-rank based tensor completion, which is a higher order extension of
matrix completion, has received considerable attention. However, the low-rank assumption …

Source separation models that make use of nonnegativity in their parameters have been
gaining increasing popularity in the last few years, spawning a significant number of …

Abstract Nonnegative Matrix Factorization (NMF) has been widely used for different
purposes such as feature learning, dictionary leaning and dimensionality reduction in data …

Nonnegative matrix factorization (NMF) is an emerging tool for meaningful low-rank matrix
representation. In NMF, explicit constraints are usually required, such that NMF generates …

In this paper, we propose a novel general non-negative matrix factorization (general-NMF)-
based digital watermarking scheme for copyright protection and integrity authentication of …

Background Plant cell walls are nanocomposites based on cellulose microfibrils embedded
in a matrix of polysaccharides and aromatic polymers. They are optimized for different …

This article introduces new multiplicative updates for nonnegative matrix factorization with
the-divergence and sparse regularization of one of the two factors (say, the activation …

This work introduces a new framework for nonnegative matrix factorization (NMF) in
multisensor or multimodal data configurations, where taking into account the mutual …

Abstract Nonnegative Matrix Factorization (NMF) as a popular technique for finding parts-
based, linear representations of nonnegative data has been successfully applied in a wide …