Coupling Recursive Hyperspheric Classification with Linear Discriminant Analysis for Improved Results.
Salyer B. ReedTyson R. C. ReedSergiu M. DascaluPublished in: ITNG (2013)
Keyphrases
- linear discriminant analysis
- feature extraction
- support vector machine svm
- small sample size
- discriminant features
- feature space
- discriminant analysis
- dimension reduction
- support vector
- class separability
- principal component analysis
- discriminative information
- class discrimination
- fisher criterion
- dimensionality reduction
- principal components analysis
- face recognition
- high dimensional data
- pattern recognition
- feature analysis
- null space
- high dimensionality
- linear discriminant
- locality preserving projections
- classification accuracy
- feature vectors
- subspace methods
- kernel discriminant analysis
- image processing
- text classification
- image classification
- nearest neighbor
- support vector machine
- decision trees
- machine learning
- sample size
- subspace analysis
- supervised dimensionality reduction
- pca lda
- generalized discriminant analysis
- dealing with high dimensional data
- involving high dimensional data
- dimensionality reduction methods
- lower dimensional
- training samples