VIF Regression: A Fast Regression Algorithm for Large Data

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.