Multidimensional Scaling is a technique to visualise similarities in datasets. It works by projecting a high-dimensional dataset into a two-dimensional space. While the resulting visualisations clearly show if samples are similar or dissimilar, they fail to communicate the why. Furthermore, the visualisations usually contain some degree of error that isn’t visible, inspiring false confidence in the resulting projections.
This project tries to solve these problems by introducing a set of interaction and visualisation techniques to examine dimensionality-reduced datasets.