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Activity Number: 359 - SPEED: Computing, Graphics, and Programming Statistics
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 11:35 AM to 12:20 PM
Sponsor: Section on Statistical Graphics
Abstract #325129
Title: Generalized Bi-Plots for Visualized Multidimensional Projections
Author(s): James Fry* and Matt Slifko and Scotland Leman
Companies: Virginia Tech and Virginia Tech and Virginia Tech
Keywords: Bi-plot ; Multidimensional Scaling ; Principal Component Analysis ; Dimension Reduction ; Stress function

Dimension reduction and visualization is a staple of data analytics. Methods such as Principal Component Analysis (PCA) and Multidimensional Scaling (MDS) provide low dimensional (LD) projections of high dimensional (HD) data while preserving an HD relationship between observations. Traditional bi-plots assign meaning to the LD space of a PCA projection by displaying LD axes for the attributes. These axes, however, are specific to the linear projection used in PCA. MDS projections, which allow for arbitrary stress and dissimilarity functions, require special care when labeling the LD space. We propose an iterative scheme to plot an LD axis for each attribute based on the user-specified stress and dissimilarity metrics. In this talk, we will discuss the details of our general bi-plot methodology, its relationship with PCA-derived bi-plots, and provide some illuminating examples based on both simulated and real data.

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

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