Reducing the Dimensionality of Hyperspectral Data using Diffusion Maps.
Luis du PlessisSteven B. DamelinMichael SearsPublished in: IGARSS (4) (2009)
Keyphrases
- diffusion maps
- hyperspectral data
- dimensionality reduction
- random projections
- hyperspectral
- manifold learning
- hyperspectral images
- principal components
- hyperspectral imagery
- high dimensional
- nonlinear dimensionality reduction
- low dimensional
- pattern recognition
- principal component analysis
- high dimensionality
- multispectral
- lower dimensional
- high dimensional data
- feature space
- feature selection
- remote sensing
- unsupervised learning
- semi supervised
- feature extraction
- principal components analysis
- data points
- sparse representation
- image classification
- dimension reduction
- action classification
- target detection
- information content
- dimensionality reduction methods
- linear discriminant analysis
- infrared
- support vector machine
- image analysis
- data sets