Iterative Non-Linear Dimensionality Reduction with Manifold Sculpting
Abstract
Many algorithms have been recently developed for reducing dimensionality by projecting data onto an intrinsic non-linear manifold. Unfortunately, existing algo- rithms often lose significant precision in this transformation. Manifold Sculpting is a new algorithm that iteratively reduces dimensionality by simulating surface tension in local neighborhoods. We present several experiments that show Man- ifold Sculpting yields more accurate results than existing algorithms with both generated and natural data-sets. Manifold Sculpting is also able to benefit from both prior dimensionality reduction efforts.
Cite
Text
Gashler et al. "Iterative Non-Linear Dimensionality Reduction with Manifold Sculpting." Neural Information Processing Systems, 2007.Markdown
[Gashler et al. "Iterative Non-Linear Dimensionality Reduction with Manifold Sculpting." Neural Information Processing Systems, 2007.](https://mlanthology.org/neurips/2007/gashler2007neurips-iterative/)BibTeX
@inproceedings{gashler2007neurips-iterative,
title = {{Iterative Non-Linear Dimensionality Reduction with Manifold Sculpting}},
author = {Gashler, Michael and Ventura, Dan and Martinez, Tony},
booktitle = {Neural Information Processing Systems},
year = {2007},
pages = {513-520},
url = {https://mlanthology.org/neurips/2007/gashler2007neurips-iterative/}
}