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

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 …

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 …

This letter describes algorithms for nonnegative matrix factorization (NMF) with the β-
divergence (β-NMF). The β-divergence is a family of cost functions parameterized by a …

The remarkable optical properties of metal nanoparticles are governed by the excitation of
localized surface plasmon resonances (LSPRs). The sensitivity of each LSPR mode, whose …

We consider inference in a general data-driven object-based model of multichannel audio
data, assumed generated as a possibly underdetermined convolutive mixture of source …

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 …

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 …

Archetypal analysis (aa) proposed by Cutler and Breiman (1994)[7] estimates the principal
convex hull (pch) of a data set. As such aa favors features that constitute representative …