Non-Linear Spectral Dimensionality Reduction Under Uncertainty.
Firas LaakomJenni RaitoharjuNikolaos PassalisAlexandros IosifidisMoncef GabboujPublished in: CoRR (2022)
Keyphrases
- dimensionality reduction
- kernel trick
- principal component analysis
- data representation
- feature extraction
- high dimensional
- high dimensionality
- high dimensional data
- pattern recognition and machine learning
- feature selection
- low dimensional
- structure preserving
- input space
- linear discriminant analysis
- hyperspectral imagery
- random projections
- data points
- nearest neighbor
- image data
- incomplete information
- hyperspectral images
- uncertain data
- pattern recognition
- decision theory
- spectral decomposition
- uncertain information
- spectral features
- neural network
- dimensionality reduction methods
- spectral analysis
- multispectral images
- principal components
- sparse representation
- data analysis
- computer vision
- learning algorithm