Dimensionality Reduction Based on kCCC and Manifold Learning.
Gengshi HuangZhengming MaTianshi LuoPublished in: J. Math. Imaging Vis. (2021)
Keyphrases
- manifold learning
- dimensionality reduction
- low dimensional
- high dimensional data
- nonlinear dimensionality reduction
- high dimensional
- diffusion maps
- subspace learning
- principal component analysis
- pattern recognition
- laplacian eigenmaps
- feature selection
- feature extraction
- locally linear embedding
- manifold learning algorithm
- feature space
- data representation
- high dimensionality
- unsupervised learning
- input space
- data points
- linear discriminant analysis
- random projections
- lower dimensional
- dimensionality reduction methods
- principal components
- locality preserving
- discriminant projection
- data sets
- graph embedding
- discriminant embedding
- preprocessing
- manifold structure
- principal components analysis
- euclidean distance
- sparse representation
- latent space
- low dimensional manifolds
- geodesic distance
- singular value decomposition
- signal processing