Stochastic discriminant analysis for linear supervised dimension reduction.
Mika JuutiFrancesco CoronaJuha KarhunenPublished in: Neurocomputing (2018)
Keyphrases
- dimension reduction
- discriminant analysis
- linear discriminant analysis
- principal component analysis
- feature extraction
- face recognition
- partial least squares
- cluster analysis
- scatter matrices
- dimensionality reduction
- supervised dimensionality reduction
- feature selection
- unsupervised learning
- dimension reduction methods
- manifold learning
- high dimensional data
- support vector
- discriminative information
- low dimensional
- supervised learning
- fisher discriminant analysis
- random projections
- feature space
- semi supervised
- learning algorithm
- principal components analysis
- preprocessing
- high dimensional
- high dimensionality
- pattern recognition
- null space
- singular value decomposition
- support vector machine svm
- image processing
- computer vision
- independent component analysis
- kernel pca
- dimensionality reduction methods
- feature vectors