The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data
Abstract
The recent development of more sophisticated remote-sensing systems enables the measurement of radiation in many more spectral intervals than was previously possible. An example of this technology is the AVIRIS system, which collects image data in 220 bands. The increased dimensionality of such hyperspectral data greatly enhances the data's information content, but provides a challenge to the current techniques for analyzing such data. Human experience in 3D space tends to mislead our intuition of geometrical and statistical properties in high-dimensional space, properties which must guide our choices in the data analysis process. Using Euclidean and Cartesian geometry, high-dimensional space properties are investigated in this paper, and their implication for high-dimensional data and its analysis is studied in order to illuminate the differences between conventional spaces and hyperdimensional space.
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Cited by
Classification of hyperspectral remote sensing images with support vector machines
Melgani, F., & Bruzzone, L. (2004) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2004.831865 · Cited by 4,411
Advances in Spectral-Spatial Classification of Hyperspectral Images
Fauvel, M., Tarabalka, Y., Benediktsson, J. A., Chanussot, J., & Tilton, J. C. (2013) · Proceedings of the IEEE · doi:10.1109/jproc.2012.2197589 · Cited by 1,282
Hyperspectral image data analysis
Landgrebe, D. (2002) · IEEE Signal Processing Magazine · doi:10.1109/79.974718 · Cited by 1,152
Self-citationAdvanced Spectral Classifiers for Hyperspectral Images: A review
Ghamisi, P., Plaza, J., Chen, Y., Li, J., & Plaza, A. J. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2016.2616418 · Cited by 675
Face recognition with radial basis function (RBF) neural networks
Er, M. J., Wu, S., Lu, J., & Toh, H. L. (2002) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2002.1000134 · Cited by 664
Metadata
- Authors
- Jimenez, L. O., & Landgrebe, D. A.
- Venue
- IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews)
- Published
- 1998
- Volume
- 28
- Issue
- 1
- Pages
- 39-54
- ISSN
- 1094-6977 (ISSN-L)
- OpenAlex
- W2303501228
- Cited by
- 417
- References
- 26
- FWCI
- 24.80
- Type
- journal article
- Language
- en
- Open access
- closed
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- Jimenez, L. O., & Landgrebe, D. A. (1998). Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews). https://doi.org/10.1109/5326.661089
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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.
