Supervised Adaptive-Transfer PLSA for Cross-Domain Text Classification

Cross-domain learning is a very promising technique to improve classification in the target (testing) domain whose data distributions are very different from the source (training) domain. Many cross-domain text classification methods are built on topic modeling approaches. However, topic model methods are unsupervised in nature without fully utilizing the label information of the source domain. In addition, almost all cross-domain learning approaches utilize the knowledge of source domain in the later stage of the training process, and this limits the knowledge transfer. In this paper, we propose a model named Supervised Adaptive transfer Probabilistic Latent Semantic Analysis (SAtPLSA) for cross-domain text classification aiming to deal with the above two issues. The proposed model extends the original PLSA to a supervised learning paradigm. By defining the common labeled information from each term across domains, we transfer knowledge in source domain to assist classifying text in target domain. In addition, we adaptively modify the weight value controlling the proportion of the usage of knowledge from source domain in the model learning process. At last, we conducted experiments on nine benchmark datasets in cross domain text classification to compare the performance of our proposed algorithm with two classical supervised learning methods and five state-of-art transfer learning approaches. The experimental results have shown the effectiveness and efficiency of our proposed SAtPLSA algorithm.