Knowl Inf Syst (2014) 38:567–597 DOI 10.1007/s10115-012-0601-y REGULAR PAPER Transfer dimensionality reduction by Gaussian process in parallel Bin Tong · Junbin Gao · Thach Nguyen Huy · Hao Shao · Einoshin Suzuki Received: 26 July 2011 / Revised: 8 August 2012 / Accepted: 19 December 2012 / Published online: 8 January 2013 © Springer-Verlag London 2013 Abstract Dimensionality reduction has been considered as one of the most significant tools for data analysis. In general, supervised information is helpful for dimensionality reduction. However, in typical real applications, supervised information in multiple source tasks may be available, while the data of the target task are unlabeled. An interesting problem of how to guide the dimensionality reduction for the unlabeled target data by exploiting useful knowl- edge, such as label information, from multiple source tasks arises in such a scenario. In this paper, we propose a new method for dimensionality reduction in the transfer learning setting. Unlike traditional paradigms where the useful knowledge from multiple source tasks is trans- ferred through distance metric, we attempt to learn a more informative mapping function between the original data and the reduced data by Gaussian process that behaves more appro- priately than other parametric regression methods due to its less parametric characteristic. This work is supported by the grant-in-aid for scientific research on fundamental research (B) 21300053 from the Japanese Ministry of Education, Culture, Sports, Science and Technology, and Charles Sturt University Competitive Research Grant OPA 4818. This work was partially supported by the National Science Foundation (Grant No. 61133016), and the National High Technology Joint Research Program of China (863 Program, Grant No. 2011AA010706). B. Tong (B ) · T. Nguyen Huy · H. Shao Graduate School of Systems Life Sciences, Kyushu University, Fukuoka, Japan e-mail: bintong@i.kyushu-u.ac.jp T. Nguyen Huy e-mail: thachnh@i.kyushu-u.ac.jp H. Shao e-mail: shaohao@i.kyushu-u.ac.jp J. Gao School of Computing and Mathematics, Charles Sturt University, Bathurst, Australia e-mail: jbgao@csu.edu.au E. Suzuki Department of Informatics, ISEE, Kyushu University, Fukuoka, Japan e-mail: suzuki@inf.kyushu-u.ac.jp 123