Subspace Regularized Linear Discriminant Analysis for Small Sample Size Problems.
Zhidong WangWuyi YangPublished in: PRICAI (2012)
Keyphrases
- small sample size
- linear discriminant analysis
- high dimensionality
- high dimensional data
- high dimensional
- dimensionality reduction
- sample size
- principal component analysis
- discriminant analysis
- null space
- face recognition
- feature space
- subspace methods
- microarray data
- scatter matrices
- feature extraction
- linear projection
- support vector
- dimension reduction
- scatter matrix
- discriminant information
- low dimensional
- feature selection
- qr decomposition
- support vector machine svm
- data sets
- subspace learning
- original data
- machine learning
- linear discriminant
- least squares
- nearest neighbor
- upper bound
- subspace clustering
- subspace analysis
- data points
- fisher criterion
- k means
- pattern recognition
- principal components
- gene expression data