Robust Regression via Online Feature Selection Under Adversarial Data Corruption
Xuchao Zhang, Shuo Lei, Liang Zhao, Arnold Boediardjo
The presence of data corruption in user-generated streaming data, such as social media, motivates a new fundamental problem that learns reliable regression coefficient when features are not accessible entirely at one time. Until now, several important challenges still cannot be handled concurrently: 1) corrupted data estimation when only partial features are accessible; 2) online feature selection when data contains adversarial corruption; and 3) scaling to a massive dataset. This paper proposes a novel RObust regression algorithm via Online Feature Selection (RoOFS) that concurrently addresses all the above challenges. Specifically, the algorithm iteratively updates the regression coefficients and the uncorrupted set via a robust online feature substitution method. Extensive empirical experiments in both synthetic and real-world data sets demonstrated that the effectiveness of our new method is superior to that of existing methods in the recovery of both feature selection and regression coefficients, with very competitive efficiency.
Xuchao Zhang, Shuo Lei, Liang Zhao, Arnold P. Boedihardjo, Chang-Tien Lu:Robust Regression via Online Feature Selection Under Adversarial Data Corruption. ICDM 2018: 1440-1445
- Date of publication:
- December 31, 2018
- IEEE International Conference on Data Mining
- Page number(s):