Spatial-Spectral Dimensionality Reduction with Partial Knowledge of Class Labels
Semi-supervised graph-based dimensionality reduction for hyperspectral image data
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Performs spatial-spectral dimensionality reduction of hyperspectral imagery with partial knowledge of class labels, followed by SVM-based classification, as described in the paper:
N. D. Cahill, S. E. Chew, and P. S. Wenger, "Spatial-Spectral Dimensionality Reduction of Hyperspectral Imagery with Partial Knowledge of Class Labels," Proc. SPIE Defense & Security: Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, April 2015.
This code requires the Spectral-Spatial Schroedinger Eigenmaps library, available at the MATLAB File Exchange, File No. 45908.
Cite As
Nathan Cahill (2026). Spatial-Spectral Dimensionality Reduction with Partial Knowledge of Class Labels (https://in.mathworks.com/matlabcentral/fileexchange/50189-spatial-spectral-dimensionality-reduction-with-partial-knowledge-of-class-labels), MATLAB Central File Exchange. Retrieved .
Acknowledgements
Inspired by: Spatial-Spectral Schroedinger Eigenmaps
General Information
- Version 1.0 (9.51 MB)
MATLAB Release Compatibility
- Compatible with any release
Platform Compatibility
- Windows
- macOS
- Linux
| Version | Published | Release Notes | Action |
|---|---|---|---|
| 1.0 |
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