Stable Sparse Subspace Embedding for Dimensionality Reduction.
Li ChenShuizheng ZhouJiajun MaPublished in: CoRR (2020)
Keyphrases
- dimensionality reduction
- high dimensional
- discriminative subspace
- locality preserving projections
- nonlinear dimensionality reduction
- low dimensional
- random projections
- sparse representation
- graph embedding
- subspace learning
- high dimensional data
- structure preserving
- principal component analysis
- neighborhood preserving
- lower dimensional
- embedding space
- low dimensional spaces
- multidimensional scaling
- manifold learning
- pattern recognition
- dimensionality reduction methods
- linear projection
- principal components
- linear dimensionality reduction
- feature space
- data points
- sparse coding
- linear discriminant analysis
- compressive sensing
- input space
- feature extraction
- euclidean distance
- vector space
- dictionary learning
- semi supervised dimensionality reduction
- discriminant information
- compressed sensing
- high dimensionality
- data representation
- dimension reduction
- locally linear embedding
- pattern recognition and machine learning
- latent space
- kernel pca
- subspace clustering
- metric learning
- feature selection
- principal components analysis
- nearest neighbor
- regularized regression
- face recognition
- rank minimization
- high dimensional spaces
- data hiding
- basis vectors
- canonical correlation analysis
- linear subspace
- data sets
- geodesic distance
- neural network