This letter presents theoretical, algorithmic, and experimental results about nonnegative
matrix factorization (NMF) with the Itakura-Saito (IS) divergence. We describe how IS-NMF is …

We describe nonnegative matrix factorisation (NMF) with a Kullback‐Leibler (KL) error
measure in a statistical framework, with a hierarchical generative model consisting of an …

This paper proposes to use exemplar-based sparse representations for noise robust
automatic speech recognition. First, we describe how speech can be modeled as a linear …

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 …

This paper presents theoretical and experimental results about constrained non-negative
matrix factorization (NMF) in a Bayesian framework. A model of superimposed Gaussian …

Recommender Systems present a high-level of sparsity in their ratings matrices. The
collaborative filtering sparse data makes it difficult to: 1) compare elements using memory …

Most patients with cancer are refractory to immune checkpoint blockade (ICB) therapy, and
proper patient stratification remains an open question. Primary patient data suffer from high …

Nonnegative matrix factorization (NMF) is an effective and popular low-rank model for
nonnegative data. It enjoys a rich background, both from an optimization and probabilistic …

A gamma process dynamic Poisson factor analysis model is proposed to factorize a dynamic
count matrix, whose columns are sequentially observed count vectors. The model builds a …

Nonnegative matrix factorization (NMF) is now a common tool for audio source separation.
When learning NMF on large audio databases, one major drawback is that the complexity in …