Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data.
Sarah Elizabeth RomanesJohn Thomas OrmerodJean Yee Hwa YangPublished in: CoRR (2018)
Keyphrases
- high dimensional data
- discriminant analysis
- linear discriminant analysis
- dimensionality reduction
- feature selection
- high dimensionality
- class separability
- dimension reduction
- feature extraction
- principal component analysis
- low dimensional
- high dimensional
- data sets
- small sample size
- subspace clustering
- data points
- nearest neighbor
- gene expression data
- covariance matrix
- original data
- feature space
- face recognition
- input space
- data analysis
- preprocessing step
- low rank
- unsupervised learning
- sparse representation
- fisher discriminant analysis
- manifold learning
- pattern recognition
- feature selection algorithms
- principal components analysis
- dimensionality reduction methods
- text classification
- multi class
- subspace learning
- model selection
- input data
- lower dimensional
- neural network
- scatter matrices
- regression problems
- microarray data
- support vector machine
- machine learning
- random projections
- locally linear embedding
- singular value decomposition
- feature set