Topic modelling is a machine learning technique that is extensively used in Natural
Language Processing (NLP) applications to infer topics within unstructured textual data …

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 …

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 …

This research presents a novel aggregating method for constructing an aggregated topic
model that is composed of the topics with greater coherence than individual models. When …

Social media provide a platform for quick and seamless access to information. However, the
propagation of false information, especially during the last year, raises major concerns …

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 …

Considering a mixed signal composed of various audio sources and recorded with a single
microphone, we consider in this paper the blind audio source separation problem which …

Nonnegative matrix factorization (NMF) based topic modeling methods do not rely on model-
or data-assumptions much. However, they are usually formulated as difficult optimization …

Motivated by the dynamics of development, in which cells of recognizable types, or pure cell
types, transition into other types over time, we propose a method of semisoft clustering that …

Biological mechanisms underlying human germline mutations remain largely unknown. We
statistically decompose variation in the rate and spectra of mutations along the genome …