A New Formulation of Linear Discriminant Analysis for Robust Dimensionality Reduction.
Haifeng ZhaoZheng WangFeiping NiePublished in: IEEE Trans. Knowl. Data Eng. (2019)
Keyphrases
- linear discriminant analysis
- dimensionality reduction
- discriminant analysis
- high dimensional data
- principal component analysis
- feature extraction
- dimension reduction
- high dimensional
- face recognition
- feature space
- linear projection
- low dimensional
- high dimensionality
- pattern recognition
- data representation
- dealing with high dimensional data
- small sample size
- subspace learning
- dimensionality reduction methods
- linear discriminant
- principal components analysis
- discriminant features
- class separability
- manifold learning
- kernel pca
- graph embedding
- feature selection
- fisher criterion
- null space
- locality preserving projections
- involving high dimensional data
- kernel discriminant analysis
- discriminative information
- principal components
- kernel learning
- singular value decomposition
- scatter matrix
- data points
- supervised dimensionality reduction
- random projections
- equivalence relationship
- qr decomposition
- feature vectors