Migratory Logistic Regression for Learning Concept Drift Between Two Data Sets With Application to UXO Sensing
Xuejun Liao, Lawrence Carin · IEEE Transactions on Geoscience and Remote Sensing · 2009
To achieve good generalization in supervised learning, the training and testing examples are usually required to be drawn from the same source distribution. In this paper, we propose a method to relax this requirement in the context of logistic regression. AssumingDpandDaare two sets of examples drawn from two different distributionsTandA(called concepts, borrowing a term from psychology), whereDaare fully labeled andDppartially labeled, our objective is to complete the labels ofDp. We introduce an auxiliary variable mu for each example inDato reflect its mismatch withDp. Under an appropriate constraint the mus are estimated as a byproduct, along with the classifier. We also present an active learning approach for selecting the labeled examples inDp. The proposed algorithm, calledmigratorylogisticregression, is demonstrated successfully on simulated data as well as on real measured data of interest for unexploded ordnance cleanup.