Geometrically local embedding in manifolds for dimension reduction.
Shuzhi Sam GeHongsheng HeChengyao ShenPublished in: Pattern Recognit. (2012)
Keyphrases
- dimension reduction
- manifold embedding
- manifold learning
- nonlinear dimensionality reduction
- low dimensional
- laplacian eigenmaps
- high dimensional
- principal component analysis
- feature extraction
- high dimensional problems
- high dimensional data
- singular value decomposition
- random projections
- dimensionality reduction
- cluster analysis
- embedding space
- high dimensionality
- linear discriminant analysis
- low dimensional manifolds
- head pose estimation
- discriminative information
- feature selection
- vector space
- feature space
- latent space
- high dimensional data analysis
- nonlinear manifold
- preprocessing
- geodesic distance
- unsupervised learning
- data points
- dimension reduction methods
- face recognition
- graph embedding
- least squares
- nearest neighbor
- qr decomposition
- pattern recognition