Maximizing Gaussianity using kurtosis measurement in the kernel space for kernel linear discriminant analysis.
E. S. Gopi SubbuPalanisamy PonnusamyPublished in: Neurocomputing (2014)
Keyphrases
- kernel space
- linear discriminant analysis
- feature space
- principal components
- principal component analysis
- discriminant analysis
- dimensionality reduction
- hyperplane
- support vector
- face recognition
- high dimensional
- dimension reduction
- high dimensional data
- feature extraction
- class separability
- input space
- null space
- independent component analysis
- maximum margin
- support vector machine svm
- kernel methods
- kernel function
- feature selection
- training samples
- generalized discriminant analysis
- data points
- feature vectors
- kernel discriminant analysis
- reduced set
- classification accuracy
- input data
- low dimensional
- upper bound
- support vector machine
- training data