Derandomized dimensionality reduction with applications.
Lars EngebretsenPiotr IndykRyan O'DonnellPublished in: SODA (2002)
Keyphrases
- dimensionality reduction
- principal component analysis
- feature extraction
- pattern recognition
- low dimensional
- data representation
- feature selection
- structure preserving
- dimensionality reduction methods
- high dimensional data
- preprocessing step
- manifold learning
- high dimensional
- high dimensionality
- principal components
- pattern recognition and machine learning
- diffusion maps
- feature space
- input space
- data points
- linear projection
- singular value decomposition
- lower dimensional
- linear dimensionality reduction
- random projections
- intrinsic dimensionality
- linear discriminant analysis
- euclidean distance
- sparse representation
- kernel pca
- computer vision
- supervised dimensionality reduction
- locally linear embedding
- multidimensional scaling
- dimension reduction
- feature vectors
- data structure