Distributed Heterogeneous Transfer Learning

Transfer learning has proved to be effective for building predictive models for a target domain, by exploiting the knowledge coming from a related source domain. However, most existing transfer learning methods assume that source and target domains have common feature spaces. Heterogeneous transfer learning methods aim to overcome this limitation, but they often make strong assumptions, e.g., on the number of features, or cannot distribute the workload when working in a big data environment. In this manuscript, we present a novel transfer learning method which: i) can work with heterogeneous feature spaces without imposing strong assumptions; ii) is fully implemented in Apache Spark following the MapReduce paradigm, enabling the distribution of the workload over multiple computational nodes; iii) is able to work also in the very challenging Positive-Unlabeled (PU) learning setting. We conducted our experiments in two relevant application domains for transfer learning: the prediction of the energy consumption in power grids and the reconstruction of gene regulatory networks. The results show that the proposed approach fruitfully exploits the knowledge coming from the source domain and outperforms 3 state-of-the-art heterogeneous transfer learning methods.