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Activity Number: 669 - New Nonparametric Statistical Methods for High-Dimensional Data
Type: Contributed
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Nonparametric Statistics
Abstract #330925 Presentation
Title: Graphical Investigation of the Geometry of High and Infinite Dimensional Data
Author(s): Wolfgang Polonik* and Gabriel Chandler
Companies: University of California, Davis and Pomona College
Keywords: graphical methods; non-linear projections; multi-dimensional scaling
Abstract:

A novel idea is presented for graphically investigating certain aspects of the geometry of high-dimensional data and, more generally, of Hilbert space-valued data. The method can be viewed as projecting the data, in a non-linear way, onto two-dimensional planes, and, given a data set of size n, the method constructs n(n-1)/2 different such projections. The usefulness of the methodology is illustrated, among others, by investigating its use in selecting a kernel for an SVM classifier.


Authors who are presenting talks have a * after their name.

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