Space-Time Local Embeddings
Abstract
Space-time is a profound concept in physics. This concept was shown to be useful for dimensionality reduction. We present basic definitions with interesting counter-intuitions. We give theoretical propositions to show that space-time is a more powerful representation than Euclidean space. We apply this concept to manifold learning for preserving local information. Empirical results on non-metric datasets show that more information can be preserved in space-time.
Cite
Text
Sun et al. "Space-Time Local Embeddings." Neural Information Processing Systems, 2015.Markdown
[Sun et al. "Space-Time Local Embeddings." Neural Information Processing Systems, 2015.](https://mlanthology.org/neurips/2015/sun2015neurips-spacetime/)BibTeX
@inproceedings{sun2015neurips-spacetime,
title = {{Space-Time Local Embeddings}},
author = {Sun, Ke and Wang, Jun and Kalousis, Alexandros and Marchand-Maillet, Stephane},
booktitle = {Neural Information Processing Systems},
year = {2015},
pages = {100-108},
url = {https://mlanthology.org/neurips/2015/sun2015neurips-spacetime/}
}