Manifold Learning and the Quantum Jensen-Shannon Divergence Kernel.
Luca RossiAndrea TorselloEdwin R. HancockPublished in: CAIP (1) (2013)
Keyphrases
- manifold learning
- jensen shannon divergence
- jensen shannon
- laplacian eigenmaps
- information theory
- information theoretic
- feature space
- dimensionality reduction
- nonlinear dimensionality reduction
- low dimensional
- feature selection
- semi supervised
- dimension reduction
- high dimensional
- feature extraction
- mutual information
- kernel function
- riemannian manifolds
- input space
- sparse representation
- locally linear embedding
- embedding space
- kernel methods
- machine learning
- high dimensional data
- selection criterion
- kernel matrix
- support vector
- kernel pca
- metric learning
- em algorithm
- computer vision