Cross domain distribution adaptation via kernel mapping

When labeled examples are limited and difficult to obtain, transfer learning employs knowledge from a source domain to improve learning accuracy in the target domain. However, the assumption made by existing approaches, that the marginal and conditional probabilities are directly related between source and target domains, has limited applicability in either the original space or its linear transformations. To solve this problem, we propose an adaptive kernel approach that maps the marginal distribution of targetdomain and source-domain data into a common kernel space, and utilize a sample selection strategy to draw conditional probabilities between the two domains closer. We formally show that under the kernel-mapping space, the difference in distributions between the two domains is bounded; and the prediction error of the proposed approach can also be bounded. Experimental results demonstrate that the proposed method outperforms both traditional inductive classifiers and the state-of...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: ErHeng Zhong, Wei Fan, Jing Peng, Kun Zhang, Jiangtao Ren, Deepak S. Turaga, Olivier Verscheure
• Keywords: Conditional Probabilities, Data Mining, KDD 2009, Target Domains, Text Categorization Problem