4 citations · 5 across the 4 of their papers we have counts for
5 papers
More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing
Henry Kvinge, Elin Farnell, Julia R. Dupuis +3
Compressive sensing (CS) is a method of sampling which permits some classes of signals to be reconstructed with high accuracy even when they were under-sampled. In this paper we ex…
Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection
Elin Farnell, Henry Kvinge, Julia R. Dupuis +3
One of the fundamental assumptions of compressive sensing (CS) is that a signal can be reconstructed from a small number of samples by solving an optimization problem with the appr…
A data-driven approach to sampling matrix selection for compressive sensing
Elin Farnell, Henry Kvinge, John P. Dixon +5
Sampling is a fundamental aspect of any implementation of compressive sensing. Typically, the choice of sampling method is guided by the reconstruction basis. However, this approac…
Persistent Homology on Grassmann Manifolds for Analysis of Hyperspectral Movies
Sofya Chepushtanova, Michael Kirby, Chris Peterson +1
The existence of characteristic structure, or shape, in complex data sets has been recognized as increasingly important for mathematical data analysis. This realization has motivat…
Classification of Hyperspectral Imagery on Embedded Grassmannians
Sofya Chepushtanova, Michael Kirby
We propose an approach for capturing the signal variability in hyperspectral imagery using the framework of the Grassmann manifold. Labeled points from each class are sampled and u…