Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets.
Henry KvingeElin FarnellMichael KirbyChris PetersonPublished in: HPEC (2018)
Keyphrases
- dimensionality reduction
- data representation
- high dimensionality
- data sets
- feature extraction
- principal component analysis
- unsupervised learning
- high dimensional
- pattern recognition and machine learning
- pattern recognition
- linear discriminant analysis
- feature selection
- principal components
- structure preserving
- dimensionality reduction methods
- hierarchical classification
- manifold learning
- linear dimensionality reduction
- random projections
- singular value decomposition
- preprocessing step
- high dimensional data
- low dimensional
- data points
- feature space
- hierarchical clustering
- neural network