Abstract Deep Nonnegative Matrix Factorization (Deep NMF) is an effective strategy for
feature extraction in recent years. By decomposing the matrix recurrently on account of the …

How to extract useful knowledge from a high-dimensional and sparse (HiDS) matrix
efficiently is critical for many big data-related applications. A latent factor (LF) model has …

This paper presents the automatic analysis of surface thermograms in response to a long-
pulsed thermography inspection to classify buried defects in composite materials. Time …

In some applications of biomedical imaging, a linear mixture model can represent the
constitutive elements (end-members) and their contributions (abundances) per pixel of the …

In this paper, we propose a new Semi-Nonnegative Matrix Factorization method for 2-
dimensional (2D) data, named TS-NMF. It overcomes the drawback of existing methods that …

Recently there has been increasing attention towards analysis dictionary learning. In
analysis dictionary learning, it is an open problem to obtain the strong sparsity-promoting …

Underdetermined blind source separation (UBSS) is a hot and challenging problem in
signal processing. In the traditional UBSS algorithm, the number of source signals is often …

In sparse representation problem, there is always interest to reduce the solution space by
introducing additional constraints. This can lead to efficient application-specific algorithms …

Denoising a single-channel speech (recorded using one microphone) remains an open
problem in many speech-related applications. Recently, supervised deep learning methods …

The sparse analysis model is an alternative approach to the sparse synthesis model that has
emerged recently. Most analysis dictionary learning problems based on the sparse analysis …