Joint optimization of feature transform and instance weighting for domain adaptation

In this paper, we propose a novel scheme for domain adaptation in which feature transform and instance weights are jointly optimized. Due to the joint optimization, we can obtain feasible feature transform for domain adaptation while we jointly eliminate source samples which are unrelated to target samples by estimating those weights. By introducing regularization which induces the weights to be homogeneous, we can increase the number of successfully adapted source samples as much as possible resulting in the stable training of classifiers after domain adaptation. Experimental results on both benchmark data and real surveillance video show that our method can achieve the same or better performance than that of state-of-the-art methods, though we used only the simplest feature transform, that is linear transform, in our method.