Distributed distance estimation for manifold learning and dimensionality reduction.
Mehmet E. YildizFrank CiaramelloAnna ScaglionePublished in: ICASSP (2009)
Keyphrases
- manifold learning
- dimensionality reduction
- distance estimation
- low dimensional
- nonlinear dimensionality reduction
- diffusion maps
- high dimensional data
- high dimensional
- pattern recognition
- feature extraction
- subspace learning
- locally linear embedding
- laplacian eigenmaps
- dimension reduction
- high dimensionality
- principal component analysis
- linear discriminant analysis
- data representation
- data points
- dimensionality reduction methods
- manifold learning algorithm
- discriminant projection
- feature selection
- input space
- similarity search
- intrinsic dimensionality
- geodesic distance
- feature space
- unsupervised learning
- locality preserving projections
- manifold structure
- principal components analysis
- lower dimensional
- neural network
- nonlinear manifold
- similarity measure
- semi supervised
- embedding space
- euclidean distance
- pairwise
- manifold embedding
- principal components
- low dimensional manifolds
- riemannian manifolds
- singular value decomposition
- sparse representation
- multiscale
- machine learning