Locality-Preserving Discriminant Analysis in Kernel-Induced Feature Spaces for Hyperspectral Image Classification.
Wei LiSaurabh PrasadJames E. FowlerLori Mann BrucePublished in: IEEE Geosci. Remote. Sens. Lett. (2011)
Keyphrases
- discriminant analysis
- locality preserving
- feature space
- linear discriminant analysis
- dimensionality reduction
- face recognition
- locality preserving projections
- kernel trick
- feature extraction
- principal component analysis
- manifold learning
- dimensionality reduction methods
- high dimensional
- kernel principal component analysis
- feature vectors
- image representation
- high dimensionality
- training set
- training samples
- random projections
- hyperplane
- kernel function
- data representation
- low dimensional
- dimension reduction
- high dimensional data
- feature selection
- euclidean distance
- hash functions
- support vector machine
- input space
- high dimensional feature space
- face images
- data points
- pattern recognition
- graph embedding
- subspace methods
- similarity search
- lower dimensional
- hyperspectral images
- metric learning
- kernel methods
- data sets
- input data
- image retrieval
- data analysis
- data mining