Extracting discriminative concepts for domain adaptation in text mining

One common predictive modeling challenge occurs in text mining problems is that the training data and the operational (testing) data are drawn from different underlying distributions. This poses a great difficulty for many statistical learning methods. However, when the distribution in the source domain and the target domain are not identical but related, there may exist a shared concept space to preserve the relation. Consequently a good feature representation can encode this concept space and minimize the distribution gap. To formalize this intuition, we propose a domain adaptation method that parameterizes this concept space by linear transformation under which we explicitly minimize the distribution difference between the source domain with sufficient labeled data and target domains with only unlabeled data, while at the same time minimizing the empirical loss on the labeled data in the source domain. Another characteristic of our method is its capability for considering multiple ...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Bo Chen, Wai Lam, Ivor Tsang, Tak-Lam Wong
• Keywords: Data Mining, Domain Adaptation Method, KDD 2009, Sufficient Labeled Data, Text Mining Problems