Semi-Supervised Hyperspectral Subspace Learning Based on a Generalized Eigenvalue Problem for Regression and Dimensionality Reduction.
Kuniaki UtoYukio KosugiGenya SaitoPublished in: IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. (2014)
Keyphrases
- subspace learning
- hyperspectral
- dimensionality reduction
- remote sensing
- hyperspectral images
- semi supervised
- multispectral
- infrared
- high dimensional data
- image data
- manifold learning
- principal component analysis
- high dimensional
- low dimensional
- pattern recognition
- data representation
- feature extraction
- feature selection
- information content
- dimensionality reduction methods
- unsupervised learning
- feature space
- satellite images
- data points
- linear discriminant analysis
- random projections
- graph embedding
- dimension reduction
- singular value decomposition
- sparse representation
- model selection
- neural network
- support vector
- partial least squares
- data sets
- principal components
- linear combination
- image analysis
- high quality
- machine learning