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Found 8 papers, 1 papers with code
Support vector machines and Radon's theorem
A support vector machine (SVM) is an algorithm that finds a hyperplane which optimally separates labeled data points in $\mathbb{R}^n$ into positive and negative classes.
More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing
In this paper we explore a phenomenon in which bandwise CS sampling of a hyperspectral data cube followed by reconstruction can actually result in amplification of chemical signals contained in the cube.
A data-driven approach to sampling matrix selection for compressive sensing
We propose a method for defining an order for a sampling basis that is optimal with respect to capturing variance in data, thus allowing for meaningful sensing at any desired level of compression.
Rare geometries: revealing rare categories via dimension-driven statistics
The first is a general lack of labeled examples of the rare class and the second is the potential non-separability of the rare class from the majority (in terms of available features).
Monitoring the shape of weather, soundscapes, and dynamical systems: a new statistic for dimension-driven data analysis on large data sets
In this paper, we propose a new statistic that we call the $\kappa$-profile for analysis of large data sets.
Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets
Intuitively, the SAP algorithm seeks to determine a projection which best preserves the lengths of all secants between points in a data set; by applying the algorithm to find the best projections to vector spaces of various dimensions, one may infer the dimension of the manifold of origination.
A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction
Dimensionality-reduction techniques are a fundamental tool for extracting useful information from high-dimensional data sets.
Endmember Extraction on the Grassmannian
Endmember extraction plays a prominent role in a variety of data analysis problems as endmembers often correspond to data representing the purest or best representative of some feature.
