Joint Principal Component and Discriminant Analysis for Dimensionality Reduction.
Xiaowei ZhaoJun GuoFeiping NieLing ChenZhihui LiHuaxiang ZhangPublished in: IEEE Trans. Neural Networks Learn. Syst. (2020)
Keyphrases
- principal components
- discriminant analysis
- dimensionality reduction
- principal component analysis
- linear discriminant analysis
- feature extraction
- dimensionality reduction methods
- face recognition
- dimension reduction
- low dimensional
- high dimensional data
- subspace learning
- high dimensionality
- high dimensional
- discriminant projection
- unsupervised learning
- principal components analysis
- partial least squares
- data representation
- feature selection
- null space
- kernel discriminant analysis
- fisher discriminant analysis
- graph embedding
- input space
- feature space
- data points
- independent component analysis
- pattern recognition
- covariance matrix
- factor analysis
- linear discriminant
- kernel pca
- scatter matrices
- fisher criterion
- kernel trick
- discriminant subspace
- hyperplane
- manifold learning
- class separability
- data analysis
- principal component regression
- logistic regression
- regression model
- feature set
- clustering algorithm
- image processing
- data mining
- data sets