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 …

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 …

X≈ WH, W∈ RM× R, H∈ RN× R,(1) to 'explain'the data matrix X, where W≥ 0, H≥ 0, and
R≤ min {M, N}. At first glance, NMF is nothing but an alternative factorization model to …

Regularization has been one of the most popular approaches to prevent overfitting in
electroencephalogram (EEG) classification of brain-computer interfaces (BCIs). The …

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 …

With the increasing availability of various sensor technologies, we now have access to large
amounts of multiblock (also called multiset, multirelational, or multiview) data that need to be …

Real-world data are often acquired as a collection of matrices rather than as a single matrix.
Such multiblock data are naturally linked and typically share some common features while at …

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 letter, we propose a new identification criterion that guarantees the recovery of the low-
rank latent factors in the nonnegative matrix factorization (NMF) generative model, under …