Subspace Least Squares Multidimensional Scaling
Abstract
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.
Cite
@article{arxiv.1709.03484,
title = {Subspace Least Squares Multidimensional Scaling},
author = {Amit Boyarski and Alex M. Bronstein and Michael M. Bronstein},
journal= {arXiv preprint arXiv:1709.03484},
year = {2017}
}
Comments
Scale Space and Variational Methods in Computer Vision: 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017