Imperial College London


Faculty of EngineeringDepartment of Computing

Chair in Machine Learning and Pattern Recognition



m.bronstein Website




569Huxley BuildingSouth Kensington Campus






BibTex format

author = {Boyarski, A and Bronstein, AM and Bronstein, MM},
doi = {10.1007/978-3-319-58771-4_54},
pages = {681--693},
title = {Subspace least squares multidimensional scaling},
url = {},
year = {2017}

RIS format (EndNote, RefMan)

AB - © Springer International Publishing AG 2017. 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 nonrigid shapes. In this regard, MDS can be thought of as a 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.
AU - Boyarski,A
AU - Bronstein,AM
AU - Bronstein,MM
DO - 10.1007/978-3-319-58771-4_54
EP - 693
PY - 2017///
SN - 0302-9743
SP - 681
TI - Subspace least squares multidimensional scaling
UR -
ER -