Less is More - Dimensionality Reduction from a Theoretical Perspective.
Nicolas BruneauSylvain GuilleyAnnelie HeuserDamien MarionOlivier RioulPublished in: CHES (2015)
Keyphrases
- dimensionality reduction
- principal component analysis
- high dimensional data
- data representation
- high dimensional
- feature extraction
- low dimensional
- linear discriminant analysis
- pattern recognition
- viewpoint
- singular value decomposition
- feature selection
- structure preserving
- cognitive scientists
- principal components
- manifold learning
- nonlinear dimensionality reduction
- image processing
- real world
- preprocessing step
- random projections
- neural network
- linear dimensionality reduction
- data points
- face recognition
- case study
- metric learning
- lower dimensional
- dimensionality reduction methods
- multiple perspectives
- real time
- pattern recognition and machine learning
- database