A General Framework for Manifold Reconstruction from Dimensionality Reduction.
Xian'en QiuZhong ZhaoGuocan FengPatrick S. P. WangPublished in: Int. J. Pattern Recognit. Artif. Intell. (2014)
Keyphrases
- dimensionality reduction
- low dimensional
- manifold learning
- lower dimensional
- high dimensional
- nonlinear dimensionality reduction
- diffusion maps
- locally linear embedding
- graph embedding
- principal component analysis
- smooth manifold
- nonlinear manifold
- high dimensional data
- pattern recognition
- manifold structure
- feature space
- subspace learning
- feature extraction
- data representation
- embedding space
- pattern recognition and machine learning
- high dimensionality
- principal components
- linear discriminant analysis
- laplacian eigenmaps
- random projections
- three dimensional
- euclidean distance
- high resolution
- feature selection
- riemannian manifolds
- reconstruction process
- dimensionality reduction methods
- data points
- dimension reduction
- sparse representation
- intrinsic dimensionality
- metric learning
- d objects
- underlying manifold
- vector space
- image reconstruction