Constrained Laplacian Eigenmap for dimensionality reduction.
Chun ChenLijun ZhangJiajun BuCan WangWei ChenPublished in: Neurocomputing (2010)
Keyphrases
- dimensionality reduction
- laplacian eigenmaps
- kernel pca
- manifold learning
- locally linear embedding
- nonlinear dimensionality reduction
- low dimensional
- high dimensional data
- dimensionality reduction methods
- high dimensional
- principal component analysis
- feature space
- preprocessing step
- high dimensionality
- euclidean distance
- feature extraction
- pattern recognition
- singular value decomposition
- linear discriminant analysis
- data points
- feature selection
- face recognition
- subspace learning
- lower dimensional
- random projections
- euclidean space
- input space
- similarity search
- principal components
- kernel methods
- dimension reduction
- multidimensional scaling
- data sets
- sparse representation
- dynamic time warping
- non stationary
- principal components analysis
- empirical mode decomposition