Parametric nonlinear dimensionality reduction using kernel t-SNE.
Andrej GisbrechtAlexander SchulzBarbara HammerPublished in: Neurocomputing (2015)
Keyphrases
- nonlinear dimensionality reduction
- laplacian eigenmaps
- mapping function
- manifold learning
- dimensionality reduction
- low dimensional
- riemannian manifolds
- high dimensional data
- maximum variance unfolding
- locally linear embedding
- kernel function
- input space
- kernel methods
- support vector
- vector space
- data sets
- feature space
- dimensionality reduction methods
- euclidean space
- principal component analysis
- reproducing kernel hilbert space
- semi supervised
- subspace learning
- kernel matrix
- feature selection
- dimension reduction