shepard diagram for high dimension data I went through this website https://www.displayr.com/goodness-of-fit-in-mds-and-t-sne-with-shepard-diagrams/ for shepard diagram.
Right now I have a 20 dimensional data with 85 samples, I used some dimension reduction methods to get a 2*85 data. And I want to use shepard diagram to assess goodness-of-fit of my dimension reduction. Then what is the value on horizontal axis and vertical axis? Right now I can only think about 85 diagrams, in each diagram, the horizontal axis is the original distance between sample i and other samples. But what if I want to include 85 samples in 1 diagram?

Each data point in the Shepard diagram represets a pair of points $(x_i,x_j)$ in your data set. In your case with $n=85$ points, there are $85\cdot(85-1)\,/\,2=3570$ pairs. For each pair, you compute two distances, the distance in the original space $d_{orig}=d(x_i,x_j)$ and the distance in the transformed 2D space $d_{trans}=d(f(x_i), f(x_j))$, where $f$ denotes the mapping from nD to 2D.
The horizontal axis is $d_{orig}$ and the vertical axis $d_{trans}$.
