Stratifying High-Dimensional Data Based on Proximity to the Convex Hull Boundary
The convex hull of a set of points,C, serves to expose extremal properties of C and can help identify elements in C of high interest. For many problems, particularly in the presence of noise, the true vertex set (and facets) may be difficult to determine. One solution is to expand the list of high interest candidates to points lying near the boundary of the convex hull. We propose a quadratic program for the purpose of stratifying points in a data cloud based on proximity to the boundary of the convex hull. For each data point, a quadratic program is solved to determine an associated weight vector. We show that the weight vector encodes geometric information concerning the points relationship to the boundary of the convex hull. The computation of the weight vectors can be carried out in parallel, and for a fixed number of points and fixed neighborhood size, the overall computational complexity of the algorithm grows linearly with dimension. As a consequence, meaningful computations can be completed on reasonably large, high-dimensional data sets.
Lori Ziegelmeier received an A.S. and A.A. from Colby Community College in 2005. She completed her B.S. in Mathematics and B.A. in Liberal Arts and History, M.S. in Mathematics, and Ph.D. in Mathematics all at Colorado State University in 2007, 2009, and 2013, respectively. Since completing her doctoral degree, she has been in the Department of Mathematics, Statistics, and Computer Science at Macalester College and is currently an Assistant Professor. Her research is in the area of geometric and topological data analysis.