Rotational Linear Discriminant Analysis Technique for Dimensionality Reduction.
Alok SharmaKuldip K. PaliwalPublished in: IEEE Trans. Knowl. Data Eng. (2008)
Keyphrases
- linear discriminant analysis
- dimensionality reduction
- discriminant analysis
- principal component analysis
- high dimensional data
- high dimensional
- low dimensional
- dimension reduction
- feature extraction
- pattern recognition
- small sample size
- dealing with high dimensional data
- linear discriminant
- discriminant features
- feature space
- dimensionality reduction methods
- principal components analysis
- data representation
- null space
- face recognition
- subspace learning
- singular value decomposition
- high dimensionality
- data points
- random projections
- lower dimensional
- input space
- discriminative information
- feature selection
- class discrimination
- linear projection
- subspace methods
- class separability
- intrinsic dimensionality
- locality preserving projections
- metric learning
- fisher criterion
- scatter matrix
- kernel pca
- manifold learning
- supervised dimensionality reduction
- kernel trick
- support vector machine svm
- independent component analysis
- kernel discriminant analysis
- qr decomposition
- discriminant projection