Discriminative globality and locality preserving graph embedding for dimensionality reduction.
Jianping GouYuanyuan YangZhang YiJiancheng LvQirong MaoYongzhao ZhanPublished in: Expert Syst. Appl. (2020)
Keyphrases
- locality preserving
- graph embedding
- dimensionality reduction
- embedding space
- locality preserving projections
- data representation
- manifold learning
- low dimensional
- high dimensional
- subspace learning
- discriminant embedding
- kernel trick
- principal component analysis
- feature extraction
- pattern recognition
- high dimensional data
- semi supervised
- label information
- feature space
- feature selection
- unsupervised learning
- dimensionality reduction methods
- data points
- linear discriminant analysis
- lower dimensional
- euclidean distance
- input space
- manifold structure
- discriminant information
- discriminative information
- latent space
- dimension reduction
- nonlinear dimensionality reduction
- active learning
- semi supervised learning
- sparse representation
- euclidean space