  Multidimensional Scaling (MDS) is one of the most popular methods for
dimensionality reduction and visualization of high dimensional data. Apart from
these tasks, it also found applications in the field of geometry processing for
the analysis and reconstruction of non-rigid shapes. In this regard, MDS can be
thought of as a \textit{shape from metric} algorithm, consisting of finding a
configuration of points in the Euclidean space that realize, as isometrically
as possible, some given distance structure. In the present work we cast the
least squares variant of MDS (LS-MDS) in the spectral domain. This uncovers a
multiresolution property of distance scaling which speeds up the optimization
by a significant amount, while producing comparable, and sometimes even better,
embeddings.
