Normalized discriminant analysis for dimensionality reduction.
Zhizheng LiangShixiong XiaYong ZhouPublished in: Neurocomputing (2013)
Keyphrases
- discriminant analysis
- dimensionality reduction
- linear discriminant analysis
- dimensionality reduction methods
- principal component analysis
- feature extraction
- kernel discriminant analysis
- high dimensional data
- low dimensional
- graph embedding
- dimension reduction
- high dimensional
- unsupervised learning
- pattern recognition
- random projections
- data representation
- data points
- discriminant projection
- fisher linear discriminant analysis
- subspace learning
- feature selection
- face recognition
- manifold learning
- factor analysis
- feature space
- lower dimensional
- fisher criterion
- preprocessing step
- class separability
- principal components
- locality preserving projections
- fisher discriminant analysis
- principal components analysis
- high dimensionality
- independent component analysis
- feature vectors
- null space
- input space
- kernel trick
- linear discriminant
- singular value decomposition
- partial least squares
- kernel pca
- locally linear embedding
- neural network
- discriminant subspace
- generalized linear
- metric learning
- support vector machine svm
- knowledge discovery
- similarity measure
- machine learning