A Spatio-Temporal Extension to Isomap Nonlinear Dimension Reduction
Abstract
We present an extension of Isomap nonlinear dimension reduction (Tenenbaum etal. 2000) for data with both spatial and temporal relationships. Our method,ST-Isomap, augments the existing Isomap framework to consider temporalrelationships in local neighborhoods that can be propagated globally via ashortest-path mechanism. Two instantiations of ST-Isomap are presented forsequentially continuous and segmented data. Results from applying ST-Isomapto real-world data collected from human motion performance and humanoid robotteleoperation are also presented.
Cite
Text
Jenkins and Mataric. "A Spatio-Temporal Extension to Isomap Nonlinear Dimension Reduction." International Conference on Machine Learning, 2004. doi:10.1145/1015330.1015357Markdown
[Jenkins and Mataric. "A Spatio-Temporal Extension to Isomap Nonlinear Dimension Reduction." International Conference on Machine Learning, 2004.](https://mlanthology.org/icml/2004/jenkins2004icml-spatio/) doi:10.1145/1015330.1015357BibTeX
@inproceedings{jenkins2004icml-spatio,
title = {{A Spatio-Temporal Extension to Isomap Nonlinear Dimension Reduction}},
author = {Jenkins, Odest Chadwicke and Mataric, Maja J.},
booktitle = {International Conference on Machine Learning},
year = {2004},
doi = {10.1145/1015330.1015357},
url = {https://mlanthology.org/icml/2004/jenkins2004icml-spatio/}
}