Hyperspectral Dimensionality Reduction by Tensor Sparse and Low-Rank Graph-Based Discriminant Analysis.
Lei PanHeng-Chao LiYang-Jun DengFan ZhangXiang-dong ChenQian DuPublished in: Remote. Sens. (2017)
Keyphrases
- dimensionality reduction
- discriminant analysis
- hyperspectral
- linear discriminant analysis
- principal component analysis
- feature extraction
- hyperspectral data
- random projections
- remote sensing
- multispectral
- hyperspectral images
- higher order tensors
- infrared
- high dimensional data
- principal components
- pattern recognition
- high dimensional
- low dimensional
- subspace projection
- dimensionality reduction methods
- dimension reduction
- target detection
- kernel discriminant analysis
- image data
- hyperspectral imagery
- data representation
- satellite images
- data points
- high dimensionality
- information content
- singular value decomposition
- feature selection
- unsupervised learning
- supervised dimensionality reduction
- manifold learning
- factor analysis
- input space
- subspace learning
- partial least squares
- face recognition
- feature space
- null space
- graph embedding
- fisher discriminant analysis
- semi supervised
- lower dimensional
- kernel trick
- sparse representation
- scatter matrices
- computer vision
- feature vectors