Penalized principal logistic regression for sparse sufficient dimension reduction.
Seung Jun ShinAndreas ArtemiouPublished in: Comput. Stat. Data Anal. (2017)
Keyphrases
- logistic regression
- dimension reduction
- high dimensional
- random projections
- loss function
- generalized linear models
- variable selection
- sparse metric learning
- principal component analysis
- feature extraction
- decision trees
- support vector
- low dimensional
- singular value decomposition
- naive bayes
- least squares
- random forests
- linear discriminant analysis
- feature selection
- dimensionality reduction
- partial least squares
- linear support vector machines
- cluster analysis
- high dimensional data
- sparse representation
- regression model
- unsupervised learning
- high dimensionality
- feature space
- maximum likelihood
- preprocessing
- reinforcement learning
- logistic regression models
- image segmentation
- data sets