Bayesian feature selection for high-dimensional linear regression via the Ising approximation with applications to genomics.
Charles K. FisherPankaj MehtaPublished in: Bioinform. (2015)
Keyphrases
- linear regression
- high dimensional
- feature selection
- generalized linear models
- high dimensionality
- least squares
- regression problems
- dimensionality reduction
- microarray data
- feature space
- partition function
- regression methods
- gene expression data
- dimension reduction
- locally weighted
- low dimensional
- linear regression model
- support vector machine
- markov random field
- information gain
- linear models
- text categorization
- ridge regression
- nonlinear regression
- feature extraction
- regression method
- regression trees
- model selection
- text classification
- data points
- support vector
- variable selection
- multivariate regression
- closed form
- microarray
- high dimensional data
- posterior distribution
- latent variables
- machine learning
- logistic regression
- parameter estimation
- principal component analysis
- worst case
- nearest neighbor
- feature selection and classification
- optical flow
- video sequences
- kernel density estimators