Less is More - Dimensionality Reduction from a Theoretical Perspective.
Nicolas BruneauSylvain GuilleyAnnelie HeuserDamien MarionOlivier RioulPublished in: IACR Cryptol. ePrint Arch. (2016)
Keyphrases
- dimensionality reduction
- principal component analysis
- high dimensional data
- theoretical analysis
- low dimensional
- feature extraction
- manifold learning
- high dimensionality
- cognitive scientists
- random projections
- theoretical basis
- data representation
- neural network
- feature space
- principal components
- database
- lower dimensional
- databases
- linear discriminant analysis
- singular value decomposition
- pattern recognition
- euclidean distance
- feature selection
- genetic algorithm
- image processing
- real world
- website
- preprocessing step
- case study
- data sets
- principal components analysis
- locally linear embedding
- structure preserving
- data points