Embedding Approximately Low-Dimensional l_2^2 Metrics into l_1.
Amit DeshpandePrahladh HarshaRakesh VenkatPublished in: FSTTCS (2016)
Keyphrases
- low dimensional
- nonlinear dimensionality reduction
- embedding space
- graph embedding
- multidimensional scaling
- vector space
- pairwise distances
- nonlinear manifold learning
- high dimensional
- euclidean space
- low dimensional spaces
- high dimensional data
- dimensionality reduction
- latent space
- manifold learning
- low dimensional structure
- principal component analysis
- data points
- laplacian eigenmaps
- feature space
- dimension reduction
- hamming space
- input space
- evaluation metrics
- feature representation
- subspace learning
- linear subspace
- high dimensional spaces
- quality metrics
- low dimensional manifolds
- information hiding
- similarity metrics
- neural network
- feature vectors
- image processing