Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding.
Guangyao ShiFulin LuoYiming TangYuan LiPublished in: Remote. Sens. (2021)
Keyphrases
- hyperspectral
- manifold structure
- dimensionality reduction
- locality preserving projections
- nonlinear dimensionality reduction
- manifold learning
- latent space
- embedding space
- low dimensional
- graph embedding
- remote sensing
- infrared
- high dimensional
- data points
- high dimensional data
- multispectral
- principal component analysis
- image data
- dimensionality reduction methods
- geometric structure
- metric learning
- high dimensionality
- principal components
- random projections
- euclidean space
- input space
- feature selection
- feature extraction
- pattern recognition
- feature space
- semi supervised learning
- linear discriminant analysis
- information content
- vector space
- sparse coding
- lower dimensional
- riemannian manifolds
- singular value decomposition
- satellite images
- spectral clustering
- unsupervised learning
- nearest neighbor
- semi supervised
- geodesic distance
- latent variables
- training data