Kernel Approximation on Manifolds I: Bounding the Lebesgue Constant.
Thomas HangelbroekFrancis J. NarcowichJoseph D. WardPublished in: SIAM J. Math. Anal. (2010)
Keyphrases
- feature space
- laplacian eigenmaps
- kernel function
- series expansion
- manifold learning
- error bounds
- kernel methods
- kernel matrix
- closed form
- low dimensional
- euclidean space
- support vector
- input space
- riemannian manifolds
- approximation error
- kernel density estimators
- feature selection
- arbitrary dimension
- nonlinear dimensionality reduction
- high dimensional
- face recognition