Dimensionality Reduction for Tukey Regression.
Kenneth L. ClarksonRuosong WangDavid P. WoodruffPublished in: ICML (2019)
Keyphrases
- dimensionality reduction
- feature extraction
- principal component analysis
- high dimensional data
- regression model
- low dimensional
- high dimensional
- input space
- pattern recognition
- feature selection
- high dimensionality
- regression problems
- subspace learning
- support vector regression
- data representation
- linear regression
- regression analysis
- dimensionality reduction methods
- feature space
- principal components
- regression algorithm
- structure preserving
- polynomial regression
- manifold learning
- linear dimensionality reduction
- linear discriminant analysis
- data points
- random projections
- model selection
- pattern recognition and machine learning
- gaussian processes
- singular value decomposition
- sparse representation
- semi supervised
- linear projection
- partial least squares
- reproducing kernel hilbert space
- frequency domain
- nonlinear dimensionality reduction
- kernel function
- regression function