When considering the problem of unmixing hyperspectral images, most of the literature in
the geoscience and image processing areas relies on the widely used linear mixing model …

Dimensionality reduction is the essence of many data processing problems, including
filtering, data compression, reduced-order modeling and pattern analysis. While traditionally …

Computational fluid dynamics (CFD) is known for producing high-dimensional
spatiotemporal data. Recent advances in machine learning (ML) have introduced a myriad …

While many low rank and sparsity-based approaches have been developed for accelerated
dynamic magnetic resonance imaging (dMRI), they all use low rankness or sparsity in input …

Quantum computers utilize the fundamentals of quantum mechanics to solve computational
problems more efficiently than traditional computers. Gate-model quantum computers are …

Kernel principal component analysis (kernel-PCA) is an elegant nonlinear extension of one
of the most used data analysis and dimensionality reduction techniques, the principal …

Mainly due to the limited spatial resolution of the data acquisition devices, hyperspectral
image pixels generally result from the mixture of several components that are present in the …

Abstract Principal Component Analysis (PCA) is perhaps the most prominent learning tool
for dimensionality reduction in pattern recognition and computer vision. However, the ℓ 2 …

In this paper, we introduce a novel approach, called Input Output Kernel Regression (IOKR),
for learning mappings between structured inputs and structured outputs. The approach …

The use of geological parameterization procedures enables high-fidelity geomodels to be
represented in terms of relatively few variables. Such parameterizations are particularly …