Regression on manifolds using kernel dimension reduction.
Jens NilssonFei ShaMichael I. JordanPublished in: ICML (2007)
Keyphrases
- dimension reduction
- manifold learning
- feature space
- partial least squares
- low dimensional
- reproducing kernel hilbert space
- high dimensional
- manifold embedding
- principle component analysis
- kernel function
- principal component analysis
- high dimensional data
- euclidean space
- support vector
- nonlinear manifold
- gaussian processes
- high dimensionality
- input space
- kernel methods
- linear discriminant analysis
- feature extraction
- random projections
- high dimensional problems
- dimensionality reduction
- variable selection
- feature selection
- riemannian manifolds
- singular value decomposition
- regression algorithm
- manifold learning algorithm
- kernel matrix
- manifold structure
- unsupervised learning
- feature vectors
- kernel pca
- preprocessing
- data points
- gaussian process
- training set
- least squares
- latent space
- head pose estimation
- model selection
- cluster analysis