Active regression with compressive-sensing based outlier mitigation for both small and large outliers
Jian De Zheng, Xiaohua Li · 2016
In this paper, a new active learning scheme is proposed for linear regression problems with the objective of resolving the insufficient training data problem and the unreliable training data labeling problem. A pool-based active regression technique is applied to select the optimal training data to label from the overall data pool. Then, compressive sensing is exploited to remove labeling errors if the errors are sparse and have large enough magnitudes, which are called large outliers. Next, in order to mitigate the non-sparse labeling errors that have relatively small magnitudes, which are called small outliers, a new technique is developed to convert them back into sparse large outliers. With both artificial and real data sets, extensive simulations are conducted to verify the robustness of the proposed scheme in training data selection and outlier suppression.