Heterogeneous transfer learning techniques for machine learning

Abstract

The main assumption in machine learning and data mining is, training the data, and the future data have the same distribution and same features. However, in many applications, in the real world, such assumptions may not be retained. For example, sometimes, we have the task of classification in the one domain of interest, but when the same data is used in another domain, it needed enough training to work in the other domain of interest. In the field of heterogeneous transfer learning, train the data in one domain and test with other domain. In this case, knowledge is transfer; if there is a successful transfer, it can significantly improve performance by avoiding the learning in the label information which is more expensive. Over the past few years, the transfer learning has become a new learning framework to address this issue and heterogeneous transfer learning is the most active research area in the recent years. In this study, we are discussing the relationship between heterogeneous transfer learning and the other machine learning methods, including the field of adaptation, learning and multitasking learning and sample selection bias, as well as the associates of variables. We also reconnoiter some main challenges for the future issue in heterogeneous transfer learning.