Sub-domain adaptation learning methodology

Abstract

Regarded as global methods, Maximum Mean Discrepancy (MMD) based transfer learning frameworks only reflect the global distribution discrepancy and structural differences between domains; they can reflect neither the inner local distribution discrepancy nor the structural differences between domains. To address this problem, a novel transfer learning framework with local learning ability, a Sub-domain Adaptation Learning Framework (SDAL), is proposed. In this framework, a Projected Maximum Local Weighted Mean Discrepancy (PMLMD) is constructed by integrating the theory and method of Local Weighted Mean (LWM) into MMD. PMLMD reflects global distribution discrepancy between domains through accumulating local distribution discrepancies between the local sub-domains in domains. In particular, we formulate in theory that PMLMD is one of the generalized measures of MMD. On the basis of SDAL, two novel methods are proposed by using Multi-label Classifiers (MLC) and Support Vector Machine (SVM). Finally, tests on artificial data sets, high dimensional text data sets and face data sets show the SDAL-based transfer learning methods are superior to or at least comparable with benchmarking methods.