Cross-Domain Knowledge Transfer in Data Mining
Qiang Yang, Hong Kong University of Science and Technology, Hong Kong
In data mining, we often encounter situations where we have an insufficient amount of high-quality data in a target domain, but we may have plenty of auxiliary data in related domains. Transfer learning aims to exploit these additional data to improve the learning performance in the target domain. In this talk, I will give an overview on some recent advances in transfer learning for challenging data mining problems. I will present structural transfer-learning solutions under heterogeneous feature representations. I will also survey cross-domain transfer learning solutions in online recommendation, social media and social network mining. I will discuss some current limitations of cross-domain transfer learning and explore possible future directions.
Qiang Yang is a professor in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology. He is an IEEE Fellow. His research interests are data mining and artificial intelligence. Qiang received his PhD from the University of Maryland, College Park in 1989. His research teams won the 2004 and 2005 ACM KDDCUP competitions on data mining. He is a vice chair of ACM SIGART, a founding Editor in Chief of the ACM Transactions on Intelligent Systems and Technology (ACM TIST), a PC Co-chair for ACM KDD 2010 and the General Chair for ACM KDD 2012 in Beijing.