Improving implementation of linear discriminant analysis for the high dimension/small sample size problem.
Jurjen Duintjer TebbensPavel SchlesingerPublished in: Comput. Stat. Data Anal. (2007)
Keyphrases
- linear discriminant analysis
- high dimension
- feature space
- high dimensional data
- discriminant analysis
- dimensionality reduction
- face recognition
- principal component analysis
- small sample size
- feature extraction
- null space
- dimension reduction
- high dimensional
- input space
- support vector
- fisher criterion
- nearest neighbor
- scatter matrix
- real valued
- support vector machine svm
- subspace methods
- small sample
- discriminative information
- feature selection
- high dimensionality
- sample size
- principal components
- pattern recognition
- kernel function
- variable selection
- input data
- low dimensional
- support vector machine
- qr decomposition