An adapted linear discriminant analysis with variable selection for the classification in high-dimension, and an application to medical data.
Le Thi KhuyenCaroline ChauxFrédéric J. P. RichardEric GuedjPublished in: Comput. Stat. Data Anal. (2020)
Keyphrases
- variable selection
- dimension reduction
- linear discriminant analysis
- medical data
- high dimension
- high dimensional data
- feature space
- high dimensional
- dimensionality reduction
- discriminant analysis
- feature extraction
- support vector
- cross validation
- principal component analysis
- support vector machine svm
- feature selection
- high dimensionality
- low dimensional
- input variables
- medical images
- face recognition
- clinical data
- classification accuracy
- real valued
- input space
- random projections
- support vector machine
- small sample
- model selection
- feature vectors
- supervised dimensionality reduction
- data sets
- nearest neighbor
- singular value decomposition
- dimensionality reduction methods
- preprocessing
- pattern recognition
- null space
- unsupervised learning
- cluster analysis
- data analysis
- decision trees
- machine learning
- naive bayes
- training samples
- image classification
- ls svm
- supervised learning
- pairwise
- learning algorithm
- scatter matrices