VIF Regression: A Fast Regression Algorithm for Large Data

• 成果类型: Article
• 署名作者: Lin, Dongyu; Foster, Dean P.; Ungar, Lyle H.
• 署名单位: University of Pennsylvania; University of Pennsylvania; University of Pennsylvania
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2011.tm10113
• 发表日期: 2011
• 页码: 232-247
• 关键词: call center; selection; variables

摘要

We propose a fast and accurate algorithm. VIF regression, for doing feature selection in large regression problems. VIP regression is extremely fast: it uses a one-pass search over the predictors and a computationally efficient method of testing each potential predictor for addition to the model. VIE regression provably avoids model overfitting, controlling the marginal false discovery rate. Numerical results show that it is much faster than any other published algorithm for regression with feature selection and is as accurate as the best of the slower algorithms.