Dimensionality Reduction for Tukey Regression.
Kenneth L. ClarksonRuosong WangDavid P. WoodruffPublished in: CoRR (2019)
Keyphrases
- unsupervised feature selection
- dimensionality reduction
- feature selection
- regression model
- high dimensional data
- high dimensional
- regression problems
- feature space
- data representation
- principal component analysis
- linear regression
- low dimensional
- feature extraction
- pattern recognition
- input space
- high dimensionality
- random projections
- subspace learning
- singular value decomposition
- linear projection
- regression analysis
- data points
- linear discriminant analysis
- regression function
- regression method
- pattern recognition and machine learning
- support vector
- regression algorithm
- dimensionality reduction methods
- partial least squares
- support vector regression
- genetic programming
- dimension reduction
- nonlinear dimensionality reduction
- neural network
- ridge regression
- principal components
- structure preserving
- locally weighted
- aggregating algorithm
- metric learning
- reproducing kernel hilbert space
- least squares
- simple linear
- discriminant analysis