SVM-based subspace optimization domain transfer method for unsupervised cross-domain time series classification

Time series classification on edge devices has received considerable attention in recent years, and it is often conducted on the assumption that the training and testing data are drawn from the same distribution. However, in practical IoT applications, this assumption does not hold due to variations in installation positions, precision error, and sampling frequency of edge devices. To tackle this problem, in this paper, we propose a new SVM-based domain transfer method called subspace optimization transfer support vector machine (SOTSVM) for cross-domain time series classification. SOTSVM aims to learn a domain-invariant SVM classifier by which (1) global projected distribution alignment jointly exploits the marginal distribution discrepancy, geometric structure, and distribution scatter to reduce the global distribution discrepancy between the source and target domains; (2) feature grouping is used to divide the features into highly transferable features (HTF) and lowly transferable features (LTF), where the importance of HTF is preserved and importance of LTF is suppressed in the domain-invariant classifier training; and (3) empirical risk minimization is constructed for improving the discrimination of the SOTSVM. In this paper, we formulate a minimization problem that integrates global projected distribution alignment, feature grouping and empirical risk minimization into the joint SVM framework, giving an effective optimization algorithm. Furthermore, we present the extension of multiple kernel SOTSVM. Experimental results on three sets of cross-domain time series datasets show that our method outperforms some state-of-the-art conventional transfer learning methods and no transfer learning methods.