Semi-supervised kernel matching for domain adaptation
In this paper, we propose a semi-supervised kernel matching method to address domain adaptation problems where the source distribution substantially differs from the target distribution. Specifically, we learn a prediction function on the labeled source data while mapping the target data points to similar source data points by matching the target kernel matrix to a submatrix of the source kernel matrix based on a Hilbert Schmidt Independence Criterion. We formulate this simultaneous learning and mapping process as a non-convex integer optimization problem and present a local minimization procedure for its relaxed continuous form. Our empirical results show the proposed kernel matching method significantly outperforms alternative methods on the task of across domain sentiment classification. Copyright
|Conference||26th AAAI Conference on Artificial Intelligence and the 24th Innovative Applications of Artificial Intelligence Conference, AAAI-12 / IAAI-12|
Xiao, M. (Min), & Guo, Y. (2012). Semi-supervised kernel matching for domain adaptation. In Proceedings of the National Conference on Artificial Intelligence (pp. 1183–1189).