Subset based least squares subspace regression in RKHS.
Luc HoegaertsJohan A. K. SuykensJoos VandewalleBart De MoorPublished in: Neurocomputing (2005)
Keyphrases
- least squares
- reproducing kernel hilbert space
- linear regression
- tikhonov regularization
- linear model
- hilbert space
- loss function
- kernel methods
- robust estimation
- learning theory
- feature space
- low dimensional
- regression model
- regression problems
- domain adaptation
- density estimation
- distance measure
- euclidean space
- gaussian process
- high dimensional data
- gaussian kernels
- high dimensional
- optical flow
- special case
- ls svm
- principal component analysis
- singular value decomposition
- kernel function
- data dependent
- random projections
- learning problems
- canonical correlation analysis
- model selection
- sparse linear
- subspace clustering
- feature extraction
- input space
- real valued
- linear subspace
- partial least squares
- semi parametric