VIF Regression: A Fast Regression Algorithm For Large Data

被引：1
|
作者：
Lin, Dongyu [1 ]
Foster, Dean P. [1 ]
机构：
[1] Univ Penn, Wharton Sch, Dept Stat, Philadelphia, PA 19104 USA
来源：
2009 9TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING | 2009年
关键词：
variable selection; stepwise regression; variance inflation factor; false discovery rate;
D O I：
10.1109/ICDM.2009.146
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We propose a fast regression algorithm that can substantially reduce the computational complexity of searching, yet retain good accuracy. It also guarantees to discover correlated features that are collectively predictive, and avoid model over-fitting. Its capability of controlling mFDR (marginal False Discovery Rate) statistically enables the one-pass. search of the fast algorithm and guarantees the accuracy of the sparse model chosen by the algorithm without cross validation. Numerical results show that our algorithm is much faster than any other algorithm and is competitively as accurate as the best but slower algorithms.
引用
收藏
页码：848 / 853
页数：6
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