LMDT: A weakly-supervised large-margin-domain-transfer for handwritten digit recognition

Performance of handwritten character recognition systems degrades significantly when they are trained and tested on different databases. In this paper, we propose a novel large margin domain transfer algorithm, which is able to jointly reduce the data distribution mismatch of training (source) and test (target) datasets, as well as learning a target classifier by relying on a set of pre-learned classifiers with the labeled source data in addition to a few available target labels. The proposed method optimizes the combination coefficients of pre-learned classifiers to obtain the minimum mismatch between results on the source and target datasets. Our method is applicable both in semi-supervised and unsupervised domain adaptation scenarios, while most of the previous competing domain adaptation methods work only in semi-supervised scenario. Experiments on adaptation to different handwritten digit datasets demonstrate that this method achieves superior classification accuracy on target sets, comparing to the state of the art methods. Quantitative evaluation shows that an unsupervised adaptation reduces the error rates by 40.2% comparing with the SVM classifier trained by the labeled samples from the source domain.