Nonparametric discriminant multi-manifold learning for dimensionality reduction.
Bo LiJun LiXiao-Ping (Steven) ZhangPublished in: Neurocomputing (2015)
Keyphrases
- manifold learning
- dimensionality reduction
- low dimensional
- principal component analysis
- locality preserving projections
- discriminant projection
- feature extraction
- nonlinear dimensionality reduction
- high dimensional data
- high dimensional
- diffusion maps
- subspace learning
- discriminant embedding
- manifold learning algorithm
- laplacian eigenmaps
- dimension reduction
- pattern recognition
- discriminant information
- linear discriminant analysis
- locally linear embedding
- discriminant analysis
- feature selection
- locality preserving
- data points
- feature space
- unsupervised learning
- graph embedding
- manifold structure
- dimensionality reduction methods
- high dimensionality
- euclidean distance
- principal components
- lower dimensional
- geodesic distance
- data representation
- input space
- euclidean space
- data analysis
- nonlinear manifold
- data mining
- semi supervised
- embedding space
- nearest neighbor
- support vector machine svm
- intrinsic dimensionality
- metric learning