Analyzing high-dimensional time-series data using kernel transfer operator eigenfunctions.
Stefan KlusSebastian PeitzIngmar SchusterPublished in: CoRR (2018)
Keyphrases
- high dimensional
- laplace beltrami
- kernel function
- feature space
- embedding space
- kernel space
- laplacian eigenmaps
- input space
- low dimensional
- dimensionality reduction
- additive models
- data points
- manifold learning
- high dimensional data
- point cloud
- multi dimensional
- shape analysis
- similarity search
- data analysis
- kernel methods
- variable selection
- riemannian manifolds
- knowledge transfer
- graph laplacian
- parameter space
- high dimensionality
- transfer learning
- heat kernel
- hilbert schmidt
- kernel principal component analysis
- support vector
- d mesh
- metric space
- dimension reduction
- kernel matrix
- reproducing kernel hilbert space
- kernel learning
- cross domain
- sparse coding
- basis functions
- random walk
- principal component analysis
- support vector machine
- face recognition