Global Versus Local Methods in Nonlinear Dimensionality Reduction
Abstract
Recently proposed algorithms for nonlinear dimensionality reduction fall broadly into two categories which have different advantages and disad- vantages: global (Isomap [1]), and local (Locally Linear Embedding [2], Laplacian Eigenmaps [3]). We present two variants of Isomap which combine the advantages of the global approach with what have previ- ously been exclusive advantages of local methods: computational spar- sity and the ability to invert conformal maps.
Cite
Text
Silva and Tenenbaum. "Global Versus Local Methods in Nonlinear Dimensionality Reduction." Neural Information Processing Systems, 2002.Markdown
[Silva and Tenenbaum. "Global Versus Local Methods in Nonlinear Dimensionality Reduction." Neural Information Processing Systems, 2002.](https://mlanthology.org/neurips/2002/silva2002neurips-global/)BibTeX
@inproceedings{silva2002neurips-global,
title = {{Global Versus Local Methods in Nonlinear Dimensionality Reduction}},
author = {Silva, Vin D. and Tenenbaum, Joshua B.},
booktitle = {Neural Information Processing Systems},
year = {2002},
pages = {721-728},
url = {https://mlanthology.org/neurips/2002/silva2002neurips-global/}
}