A kernel view of the dimensionality reduction of manifolds.
Jihun HamDaniel D. LeeSebastian MikaBernhard SchölkopfPublished in: ICML (2004)
Keyphrases
- dimensionality reduction
- laplacian eigenmaps
- manifold learning
- low dimensional
- feature space
- kernel pca
- high dimensional
- nonlinear dimensionality reduction
- kernel learning
- kernel trick
- input space
- feature extraction
- kernel discriminant analysis
- high dimensionality
- high dimensional data
- principal component analysis
- kernel function
- kernel methods
- diffusion maps
- data representation
- class separability
- manifold structure
- embedding space
- graph embedding
- computer vision
- principal components
- pattern recognition and machine learning
- low dimensional spaces
- feature selection
- underlying manifold
- structure preserving
- linear discriminant analysis
- euclidean distance
- dimensionality reduction methods
- data points
- locally linear embedding
- neighborhood preserving
- arbitrary dimension
- latent space
- riemannian manifolds
- higher dimensional
- multiple views
- pattern recognition
- reproducing kernel hilbert space
- component analysis
- euclidean space
- singular value decomposition
- cell complexes
- sparse representation
- nonlinear manifold
- neural network