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 …

The tensor tubal rank, defined based on the tensor singular value decomposition (t-SVD),
has obtained promising results in hyperspectral image (HSI) denoising. However, the …

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 …

Blind hyperspectral unmixing (HU), also known as unsupervised HU, is one of the most
prominent research topics in signal processing (SP) for hyperspectral remote sensing [1],[2] …

As a preprocessing step, hyperspectral image (HSI) restoration plays a critical role in many
subsequent applications. Recently, based on the framework of subspace representation and …

The spectrum cartography (SC) technique constructs multi-domain (eg, frequency, space,
and time) radio frequency (RF) maps from limited measurements, which can be viewed as …

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 …

Image denoising is often empowered by accurate prior information. In recent years, data-
driven neural network priors have shown promising performance for RGB natural image …

This paper considers volume minimization (VolMin)-based structured matrix factorization.
VolMin is a factorization criterion that decomposes a given data matrix into a basis matrix …

Hyperspectral images (HSIs) are often contaminated by several types of noise, which
significantly limits the accuracy of subsequent applications. Recently, low-rank modeling …