The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A spatio-temporal extension to Isomap nonlinear dimension reduction
Abstract
We present an extension of Isomap nonlinear dimension reduction (Tenenbaum et al., 2000) for data with both spatial and temporal relationships. Our method, ST-Isomap, augments the existing Isomap framework to consider temporal relationships in local neighborhoods that can be propagated globally via a shortest-path mechanism. Two instantiations of ST-Isomap are presented for sequentially continuous and segmented data. Results from applying ST-Isomap to real-world data collected from human motion performance and humanoid robot teleoperation are also presented.
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Cites
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
Cited by
Gaussian Process Dynamical Models for Human Motion
Wang, J. M., Fleet, D. J., & Hertzmann, A. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1167 · Cited by 1,020
On Human Motion Prediction Using Recurrent Neural Networks
Martinez, J., Black, M. J., & Romero, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.497 · Cited by 984
Incremental nonlinear dimensionality reduction by manifold learning
Law, M. H. C., & Jain, A. K. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.56 · Cited by 284
Metadata
- Authors
- Jenkins, O. C., & Matarić, M. J.
- Venue
- Twenty-first international conference on Machine learning - ICML '04
- Publisher
- ACM Press
- Published
- 2004
- Pages
- 56
- OpenAlex
- W2030991373
- Cited by
- 187
- References
- 2
- Type
- conference abstract
- Language
- en
- Open access
- closed
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