Coupled locality discriminant analysis with globality preserving for dimensionality reduction.
Shuzhi SuGang ZhuYanmin ZhuBin GeXingzhu LiangPublished in: Appl. Intell. (2023)
Keyphrases
- discriminant analysis
- dimensionality reduction
- linear discriminant analysis
- principal component analysis
- dimensionality reduction methods
- feature extraction
- kernel discriminant analysis
- graph embedding
- high dimensional data
- pattern recognition
- face recognition
- high dimensional
- locality preserving
- factor analysis
- principal components analysis
- singular value decomposition
- fisher criterion
- low dimensional
- discriminant projection
- unsupervised learning
- class separability
- partial least squares
- subspace learning
- feature space
- linear discriminant
- data representation
- data points
- feature selection
- high dimensionality
- preprocessing step
- input space
- manifold learning
- fisher linear discriminant analysis
- dimension reduction
- null space
- discriminant information
- discriminant subspace
- kernel pca
- fisher discriminant analysis
- sparse representation
- generalized linear
- lower dimensional
- support vector
- independent component analysis
- cluster analysis
- locality preserving projections
- principal components
- random projections