Step 2: Layout with SVD
We transform the original distance matrix and obtain the required points in space by applying singular value decomposition.
Solution may be higher-dimensional, but components with higher impact come first, so that cutting all but the first three dimensions leads to the small error.
Con: Dimension reduction leads to error