Intrinsic Protein Distribution on Manifolds Embedded in Low-Dimensional Space.
Wei-Chen ChengPublished in: ICSI (2) (2012)
Keyphrases
- low dimensional
- low dimensional manifolds
- high dimensional
- manifold learning
- euclidean space
- dimensionality reduction
- high dimensional data
- higher dimensional
- manifold structure
- underlying manifold
- feature space
- principal component analysis
- dimension reduction
- geometric structure
- data points
- vector space
- lower dimensional
- input space
- nonlinear dimensionality reduction
- intrinsic dimension
- latent space
- embedding space
- laplacian eigenmaps
- pairwise distances
- low dimensional spaces
- intrinsic dimensionality
- manifold learning algorithm
- riemannian manifolds
- low dimensionality
- diffusion maps
- graph embedding
- multidimensional scaling
- high dimensional spaces
- linear subspace
- protein sequences
- locally linear
- protein structure prediction
- feature selection
- protein structure
- nonlinear manifold