Noise-adjusted subspace linear discriminant analysis for hyperspectral-image classification.
Wei LiSaurabh PrasadJames E. FowlerQian DuPublished in: WHISPERS (2012)
Keyphrases
- linear discriminant analysis
- principal component analysis
- hyperspectral image classification
- dimensionality reduction
- null space
- high dimensional data
- subspace methods
- discriminant analysis
- principal components analysis
- linear projection
- feature space
- qr decomposition
- feature extraction
- face recognition
- scatter matrices
- subspace analysis
- active learning
- locality preserving projections
- dimension reduction
- scatter matrix
- low dimensional
- hyperspectral
- small sample size
- support vector
- high dimensional
- discriminant information
- fisher criterion
- support vector machine svm
- hyperspectral images
- subspace learning
- principal components
- kernel pca
- independent component analysis
- input data
- methods such as principal component analysis
- linear subspace
- data points
- face images
- feature selection
- hyperspectral data
- subspace clustering
- nearest neighbor
- learning algorithm
- data sets