a generalized kernel-based random k-samplesets method for transfer learning

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.