1 citations · 1 across the 4 of their papers we have counts for
8 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…
Rare geometries: revealing rare categories via dimension-driven statistics
Henry Kvinge, Elin Farnell, Jingya Li +1
In many situations, classes of data points of primary interest also happen to be those that are least numerous. A well-known example is detection of fraudulent transactions among t…
Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large data sets
Henry Kvinge, Elin Farnell, Michael Kirby +1
Dimensionality-reduction methods are a fundamental tool in the analysis of large data sets. These algorithms work on the assumption that the "intrinsic dimension" of the data is ge…
Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets
Henry Kvinge, Elin Farnell, Michael Kirby +1
A fundamental question in many data analysis settings is the problem of discerning the "natural" dimension of a data set. That is, when a data set is drawn from a manifold (possibl…