The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
The Isomap Algorithm and Topological Stability
Abstract
Tenenbaum et al . ([1][1]) presented an algorithm, Isomap, for computing a quasi-isometric, low-dimensional embedding of a set of high-dimensional data points. Two issues need to be raised concerning this work. First, the basic approach presented by Tenenbaum et al . is not new, having been
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Citations
Cites
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
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Cai, H., Zheng, V. W., & Chang, K. C.-C. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2807452 · Cited by 2,034
Visualizing structure and transitions in high-dimensional biological data
Moon, K. R., van Dijk, D., Wang, Z., Gigante, S., Burkhardt, D. B., Chen, W. S., Yim, K., Elzen, A. V. D., Hirn, M. J., Coifman, R. R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0336-3 · Cited by 1,065
Feature dimensionality reduction: a review
Jia, W., Sun, M., Lian, J., & Hou, S. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00637-x · Cited by 797
Emotional state classification from EEG data using machine learning approach
Wang, X.-W., Nie, D., & Lu, B.-L. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.06.046 · Cited by 755
Lin, T., & Zha, H. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70735 · Cited by 446
Metadata
- Authors
- Balasubramanian, M., & Schwartz, E. L.
- Venue
- Science
- Published
- 2002-01-04
- Volume
- 295
- Issue
- 5552
- Pages
- 7-7
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2093402979
- Cited by
- 839
- References
- 1
- FWCI
- 10.62
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
