Constrained low-rank matrix approximations have been known for decades as powerful
linear dimensionality reduction techniques able to extract the information contained in large …

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 …

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 …

Due to the low resolution of hyperspectral images (HSIs), the problem of mixed pixels is
common, and hyperspectral unmixing (HU) is a crucial technology to solve the problem of …

In this paper, we introduce a new algorithm based on archetypal analysis for blind
hyperspectral unmixing, assuming linear mixing of endmembers. Archetypal analysis is a …

Hyperspectral unmixing (HU) is widely used to process mixed pixels as an essential
technology. Among them, the nonnegative matrix factorization (NMF)-based approach is one …

Data-driven anomaly detection is one of the central issues for the implementation of
predictive maintenance in cyber-physical systems (CPS). The increasing nonstationary …

Archetypal analysis is a matrix factorization method with convexity constraints. Due to local
minima, a good initialization is essential, but frequently used initialization methods yield …

High-dimensional datasets manifest as r-separable matrices in a wide range of applications
such as hyperspectral imaging, text mining, astronomy, and archetypal analysis of scientific …

abstract Constrained low-rank matrix approximations have been known for decades as
powerful linear dimensionality reduction techniques able to extract the information contained …