Manifold learning: Dimensionality reduction and high dimensional data reconstruction via dictionary learning.
Zhong ZhaoGuocan FengJiehua ZhuQi ShenPublished in: Neurocomputing (2016)
Keyphrases
- high dimensional data
- manifold learning
- dictionary learning
- dimensionality reduction
- sparse representation
- low dimensional
- sparse coding
- high dimensional
- nonlinear dimensionality reduction
- high dimensionality
- pattern recognition
- subspace learning
- input space
- unsupervised learning
- dimension reduction
- random projections
- similarity search
- data points
- principal component analysis
- feature space
- image patches
- feature extraction
- lower dimensional
- dimensional data
- linear discriminant analysis
- geodesic distance
- high dimensional spaces
- locally linear embedding
- diffusion maps
- intrinsic dimensionality
- singular value decomposition
- feature selection
- dimensionality reduction methods
- data analysis
- low rank
- metric learning
- riemannian manifolds
- face recognition
- graph embedding
- data sets
- low resolution
- nearest neighbor
- high resolution
- feature vectors
- image representation
- linear combination
- manifold structure
- image processing