 Solution

Classical multidimensional scaling begins with a symmetric dissimilarity matrix $D$, rather than necessarily with measured coordinates. Its aim is to find a low-dimensional Euclidean configuration representing those dissimilarities. Square the entries, not the matrix product, and defineFor centered coordinates $X$, squared Euclidean distances satisfy $D_{ij}^2=B_{ii}+B_{jj}-2B_{ij}$ with $B=X{X}^T$. Multiplication on both sides by $J$ annihilates the two single-index terms, proving the double-centering formula.

If $B$ is positive semidefinite, use its spectral decomposition $B=U\Lambda U^T$ to constructKeeping all positive eigenvalues reproduces the original Euclidean distances exactly; their number is the minimal embedding dimension. Keeping fewer gives a low-rank approximation to the Gram matrix. It minimizes squared Gram-matrix error, often called strain, rather than necessarily the sum of squared errors in raw distances. Negative eigenvalues signal that the dissimilarities cannot be represented exactly by Euclidean coordinates; discarding them produces an approximation whose adequacy should be inspected.

In the upper-right sketch, the supplied distances come from four points in a rectangle, and the two-dimensional reconstruction recovers that shape. The overall translation, rotation and reflection of the configuration are not identifiable from distances. Classical scaling reconstructs centered geometry from pairwise distances. If the input distances are computed from centered multivariate observations, classical scaling and principal component analysis give the same score configuration up to an orthogonal transformation: their matrices $Z{Z}^T$ and $Z^{T}Z$ share the nonzero squared singular values.