Local Dimensionality Reduction for Non-Parametric Regression.
Heiko HoffmannStefan SchaalSethu VijayakumarPublished in: Neural Process. Lett. (2009)
Keyphrases
- dimensionality reduction
- regression model
- gaussian processes
- semi parametric
- high dimensional data
- regression problems
- pattern recognition
- high dimensional
- low dimensional
- high dimensionality
- data representation
- manifold learning
- linear regression
- pattern recognition and machine learning
- random projections
- model selection
- feature selection
- principal component analysis
- dimensionality reduction methods
- input space
- feature extraction
- lower dimensional
- nonlinear dimensionality reduction
- data points
- gaussian process
- locally linear embedding
- regression algorithm
- subspace learning
- structure preserving
- support vector regression
- locally weighted
- linear discriminant analysis
- ridge regression
- partial least squares
- linear dimensionality reduction
- polynomial regression
- regression method
- feature space
- principal components
- regression methods
- image processing
- kernel pca
- genetic programming
- pairwise
- nearest neighbor
- least squares
- data sets