title: High-Dimensional Structured Feature Screening Using Binary Markov Random Fields.
abstract: Feature screening is a useful feature selection approach for high-dimensional data when the goal is to identifyallthe features relevant to the response variable. However, common feature screening methods do not take into account the correlation structure of the covariate space. We propose the concept of afeature relevance network, a binary Markov random field to represent the relevance of each individual feature by potentials on the nodes, and represent the correlation structure by potentials on the edges. By performing inference on the feature relevance network, we can accordingly select relevant features. Our algorithm does not yield sparsity, which is different from the particular popular family of feature selection approaches based on penalized least squares or penalized pseudo-likelihood. We give one concrete algorithm under this framework and show its superior performance over common feature selection methods in terms of prediction error and recovery of the truly relevant features on real-world data and synthetic data.
authors.affiliation: Department of Computer Sciences Univ. of Wisconsin-Madison.
authors.forename: Jie
authors.initials: J
authors.lastname: Liu
authors.forename: Peggy
authors.initials: P
authors.lastname: Peissig
authors.forename: Chunming
authors.initials: C
authors.lastname: Zhang
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Burnside
authors.forename: Catherine
authors.initials: C
authors.lastname: McCarty
authors.forename: David
authors.initials: D
authors.lastname: Page