Nonlinear feature extraction of hyperspectral data based on locally linear embedding (LLE).
Tian HanDavid G. GoodenoughPublished in: IGARSS (2005)
Keyphrases
- locally linear embedding
- hyperspectral data
- dimensionality reduction
- random projections
- feature extraction
- locally linear
- manifold learning
- principal component analysis
- nonlinear dimensionality reduction
- hyperspectral
- low dimensional
- hyperspectral imagery
- principal components
- hyperspectral images
- dimension reduction
- dimensionality reduction methods
- high dimensional feature space
- high dimensional data
- kernel pca
- high dimensional
- laplacian eigenmaps
- underlying manifold
- pattern recognition
- linear discriminant analysis
- subspace learning
- feature vectors
- multispectral
- feature selection
- high dimensionality
- infrared
- input space
- remote sensing
- principal components analysis
- preprocessing
- data points
- sparse representation
- dimensional data
- manifold learning algorithm
- face recognition
- support vector machine svm
- unsupervised learning
- feature space
- image processing
- discriminant analysis
- image classification
- covariance matrix
- preprocessing step
- principle component analysis
- singular value decomposition
- machine learning