Randomized Locality-Preserving Discriminant Analysis for Dimensionality Reduction and Hyperspectral Image Classification.
Vineetha MenonPublished in: IGARSS (2019)
Keyphrases
- locality preserving
- discriminant analysis
- dimensionality reduction
- linear discriminant analysis
- dimensionality reduction methods
- principal component analysis
- locality preserving projections
- feature extraction
- kernel trick
- manifold learning
- data representation
- face recognition
- high dimensional data
- high dimensional
- pattern recognition
- dimension reduction
- low dimensional
- graph embedding
- subspace learning
- embedding space
- input space
- unsupervised learning
- feature selection
- feature space
- manifold structure
- high dimensionality
- sparse representation
- fisher discriminant analysis
- principal components analysis
- subspace methods
- random projections
- principal components
- lower dimensional
- locally linear embedding
- metric learning
- singular value decomposition
- data points
- computer vision
- kernel pca
- discriminative information
- independent component analysis