Orthogonal linear discriminant analysis and feature selection for micro-array data classification.
Loris NanniAlessandra LuminiPublished in: Expert Syst. Appl. (2010)
Keyphrases
- linear discriminant analysis
- feature selection
- class separability
- feature extraction
- dimension reduction
- support vector
- feature reduction
- feature space
- small sample size
- dimensionality reduction
- discriminant analysis
- support vector machine svm
- discriminant features
- discriminative information
- high dimensionality
- class discrimination
- classification accuracy
- face recognition
- principal component analysis
- linear discriminant
- fisher criterion
- dealing with high dimensional data
- support vector machine
- text classification
- discriminant vectors
- feature set
- discriminative features
- high dimensional data
- supervised dimensionality reduction
- discriminant projection
- text categorization
- null space
- feature vectors
- locality preserving projections
- subspace analysis
- machine learning
- dimensionality reduction methods
- image classification
- subspace methods
- feature selection algorithms
- decision trees
- scatter matrix
- svm classifier
- naive bayes
- model selection
- high dimensional
- selected features
- pattern recognition
- feature subset
- microarray data
- scatter matrices
- multi class
- unsupervised learning