  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 biplots 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. We discuss the details of our
general biplot methodology, its relationship with PCA-derived biplots, and
provide examples using real data.
