9 citations · 10 across the 6 of their papers we have counts for
6 papers
Regularized coordinate minimization for nonconvex composite optimization with application to quantized image compression
Daniela Lupu, George T. Samoila, Adina M. Florea +1
This paper presents a regularized cyclic coordinate minimization method for solving nonconvex composite optimization problems having the objective function formed as the sum of two…
Hyperspectral Unmixing Hierarchies
Joseph L. Garrett, P. S. Vishnu, Pauliina Salmi +4
Unmixing reveals the spatial distribution and spectral details of different constituents, called endmembers, in a hyperspectral image. Because unmixing has limited ground truth req…
Coordinate projected gradient descent minimization and its application to orthogonal nonnegative matrix factorization
Flavia Chorobura, Daniela Lupu, Ion Necoara
In this paper we consider large-scale composite nonconvex optimization problems having the objective function formed as a sum of three terms, first has block coordinate-wise Lipsch…
Quick unsupervised hyperspectral dimensionality reduction for earth observation: a comparison
Daniela Lupu, Joseph L. Garrett, Tor Arne Johansen +2
Dimensionality reduction can be applied to hyperspectral images so that the most useful data can be extracted and processed more quickly. This is critical in any situation in which…
A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction
Jon Alvarez Justo, Daniela Lupu, Milica Orlandic +2
Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission. Compressive Sensing has been used in th…
Exact representation and efficient approximations of linear model predictive control laws via HardTanh type deep neural networks
Daniela Lupu, Ion Necoara
Deep neural networks have revolutionized many fields, including image processing, inverse problems, text mining and more recently, give very promising results in systems and contro…