Electrical and Computer Engineering School, Shiraz University, Shiraz, I. R. of Iran
Transfer learning allows the knowledge transference from the source (training dataset) to target (test dataset) domain. Feature selection for transfer learning (f-MMD) is a simple and effective transfer learning method, which tackles the domain shift problem. f-MMD has good performance on small-sized datasets, but it suffers from two major issues: i) computational efficiency and predictive performance of f-MMD is challenged by the application domains with large number of examples and features, and ii) f-MMD considers the domain shift problem in fully unsupervised manner. In this paper, we propose a new approach to break down the large initial set of samples into a number of small-sized random subsets, called samplesets. Moreover, we present a feature weighting and instance clustering approach, which categorizes the original feature samplesets into the variant and invariant features. In domain shift problem, invariant features have a vital role in transferring knowledge across domains. The proposed method is called RAkET (RAndom k samplesETs), where k is a parameter that determines the size of the samplesets. Empirical evidence indicates that RAkET manages to improve substantially over f-MMD, especially in domains with large number of features and examples. We evaluate RAkET against other well-known transfer learning methods on synthetic and real world datasets.