Stable sparse subspace embedding for dimensionality reduction.
Li ChenShuisheng ZhouJiajun MaPublished in: Knowl. Based Syst. (2020)
Keyphrases
- dimensionality reduction
- high dimensional
- discriminative subspace
- locality preserving projections
- low dimensional
- nonlinear dimensionality reduction
- random projections
- sparse representation
- graph embedding
- high dimensional data
- subspace learning
- principal component analysis
- embedding space
- low dimensional spaces
- lower dimensional
- multidimensional scaling
- latent space
- structure preserving
- linear discriminant analysis
- dimensionality reduction methods
- manifold learning
- linear projection
- neighborhood preserving
- high dimensionality
- linear dimensionality reduction
- feature space
- feature extraction
- data representation
- compressed sensing
- input space
- semi supervised dimensionality reduction
- vector space
- metric learning
- pattern recognition
- feature selection
- compressive sensing
- singular value decomposition
- dimension reduction
- principal components analysis
- basis vectors
- dictionary learning
- data points
- principal components
- subspace clustering
- discriminant information
- euclidean distance
- nearest neighbor
- semi supervised
- face recognition
- canonical correlation analysis
- kernel pca
- machine learning
- orthogonal matching pursuit
- kernel trick
- sparse coding
- locally linear embedding
- geodesic distance
- data hiding