Unsupervised Geometric Learning of Hyperspectral Images
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Summary
An unsupervised learning technique that combines a density-based estimation of class modes with partial least squares regression (PLSR) on the learned modes to achieve performance comparable to fully supervised PLSR is proposed.
- Type
- preprint
- Published
- 2017-04-26
- Cited by
- 2
- References
- 51
- Access
- Open access
- OpenAlex
- https://openalex.org/W2611792427
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:31161483
Keywords
Hyperspectral imaging, Artificial intelligence, Pattern recognition (psychology), Dimensionality reduction, Cluster analysis
References
- All of Statistics: A Concise Course in Statistical Inference
- Experiments with Random Projection
- Nonlinear Hyperspectral Unmixing With Robust Nonnegative Matrix Factorization
- Determination of reaction coordinates via locally scaled diffusion map.
- Adaptive subspace detectors
- SIMPLS: an alternative approach to partial least squares regression
- Medical hyperspectral imaging: a review
- Advances in Spectral-Spatial Classification of Hyperspectral Images
- Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps.
- Unsupervised Nearest Neighbors Clustering With Application to Hyperspectral Images
- Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles
- Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression
- Beyond kappa: A review of interrater agreement measures
- Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing
- Hierarchical Clustering of Hyperspectral Images Using Rank-Two Nonnegative Matrix Factorization
- Semisupervised Neural Networks for Efficient Hyperspectral Image Classification
- Hyperspectral subpixel target detection using the linear mixing model
- Independent component analysis: algorithms and applications
- Nearest Regularized Subspace for Hyperspectral Classification
- Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
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