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

Techniques from sparse signal representation are beginning to see significant impact in
computer vision, often on nontraditional applications where the goal is not just to obtain a …

This paper considers regularized block multiconvex optimization, where the feasible set and
objective function are generally nonconvex but convex in each block of variables. It also …

Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional
representation of a dataset that lends itself to a clustering interpretation. It is possible that the …

Matrix factorization techniques have been frequently applied in information retrieval,
computer vision, and pattern recognition. Among them, Nonnegative Matrix Factorization …

We consider the problem of automatically recognizing human faces from frontal views with
varying expression and illumination, as well as occlusion and disguise. We cast the …

Motivation MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and
various cellular processes. The identification of disease-related miRNAs provides great …

A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

In these lecture notes, the authors have outlined several approaches to solve a NMF/NTF
problem. The following main conclusions can be drawn: 1) Multiplicative algorithms are not …

Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-
based, linear representations of non-negative data. Although it has successfully been …