Dimensionality Reduction Hybridizations with Multi-dimensional Scaling.
Oliver KramerPublished in: WSOM (2016)
Keyphrases
- multi dimensional scaling
- dimensionality reduction
- principal component analysis
- euclidean space
- low dimensional
- high dimensional data
- high dimensional
- euclidean distance
- data points
- low dimensional manifolds
- random projections
- manifold learning
- feature space
- linear discriminant analysis
- high dimensionality
- feature selection
- dimensionality reduction methods
- input space
- feature extraction
- lower dimensional
- face images
- singular value decomposition
- image processing
- subspace learning
- intrinsic dimensionality
- kernel pca
- shape analysis
- high dimensional spaces
- metric learning
- sparse representation
- nonlinear dimensionality reduction
- dimension reduction
- discriminant analysis
- pattern recognition
- face recognition
- data sets
- laplacian eigenmaps
- feature vectors
- neural network