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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…
A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction
Henry Kvinge, Elin Farnell, Michael Kirby +1
Dimensionality-reduction techniques are a fundamental tool for extracting useful information from high-dimensional data sets. Because secant sets encode manifold geometry, they are…
Endmember Extraction on the Grassmannian
Elin Farnell, Henry Kvinge, Michael Kirby +1
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 fea…