Approximate Orthogonal Sparse Embedding for Dimensionality Reduction.
Zhihui LaiWai Keung WongYong XuJian YangDavid ZhangPublished in: IEEE Trans. Neural Networks Learn. Syst. (2016)
Keyphrases
- dimensionality reduction
- high dimensional
- nonlinear dimensionality reduction
- structure preserving
- random projections
- graph embedding
- sparse representation
- discriminant projection
- low dimensional
- embedding space
- locality preserving projections
- multidimensional scaling
- high dimensional data
- high dimensionality
- nearest neighbor searching
- manifold learning
- low dimensional spaces
- sparse data
- data representation
- data points
- laplacian eigenmaps
- pattern recognition
- lower dimensional
- feature selection
- feature extraction
- subspace learning
- linear discriminant analysis
- sparse coding
- linear dimensionality reduction
- vector space
- dimensionality reduction methods
- input space
- locally linear embedding
- principal components
- metric learning
- feature space
- euclidean distance
- kernel pca
- compressive sensing
- intrinsic dimensionality
- dimension reduction
- latent space
- kernel learning
- high dimensional spaces
- dictionary learning
- neural network
- neighborhood preserving