Domain Adaptation via Learning Using Statistical Invariant

Domain adaptation has found widespread applications in real-life scenarios, especially when the target domain has limited labeled samples. However, most of the domain adaptation models only utilize one type of knowledge from the source domain, which is usually achieved by strong mode of convergence. To fully incorporate multiple knowledge from the source domain, for binary classification, this paper studies a novel learning paradigm for Domain Adaptation via Learning Using Statistical Invariant by simultaneously combining the strong and weak modes of convergence in a Hilbert space. The strong mode of convergence undertakes the mission of learning a least squares probability output binary classification task in a general hypothesis space, while the weak mode of convergence integrates diverse knowledge by constructing meaningful statistical invariants that embody the concept of intelligence. The utilization of weak convergence shrinks the admissible set of approximation functions, and subsequently accelerates the learning process. In this paper, several statistical invariants that represent sample, feature and parameter information from the source domain are constructed. By taking an appropriate statistical invariant, DLUSI realizes some existing methods. Experimental results on synthetic data as well as the widely used Amazon Reviews and 20 News data demonstrate the superiority of the proposed method.