Constrained low-rank matrix approximations have been known for decades as powerful
linear dimensionality reduction techniques able to extract the information contained in large …

Patient similarity networks (PSNs), where patients are represented as nodes and their
similarities as weighted edges, are being increasingly used in clinical research. These …

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 …

Non-negative matrix factorization (NMF) has found numerous applications, due to its ability
to provide interpretable decompositions. Perhaps surprisingly, existing results regarding its …

In this paper, we study the nonnegative matrix factorization problem under the separability
assumption (that is, there exists a cone spanned by a small subset of the columns of the …

In e-learning, one of the main difficulties is recommending learning materials that users can
complete on time. It becomes more challenging when users cannot devote enough time to …

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 …

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 …

A common thread in various approaches for model reduction, clustering, feature extraction,
classification, and blind source separation (BSS) is to represent the original data by a lower …