NeurIPS 2015poster18 citations
Space-Time Local Embeddings
Ke Sun, Jun Wang, Alexandros Kalousis, Stephane Marchand-Maillet
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.
BibTeX
@inproceedings{NIPS2015_7cbbc409,
author = {Sun, Ke and Wang, Jun and Kalousis, Alexandros and Marchand-Maillet, Stephane},
booktitle = {Advances in Neural Information Processing Systems},
editor = {C. Cortes and N. Lawrence and D. Lee and M. Sugiyama and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Space-Time Local Embeddings},
url = {https://proceedings.neurips.cc/paper_files/paper/2015/file/7cbbc409ec990f19c78c75bd1e06f215-Paper.pdf},
volume = {28},
year = {2015}
}