Transfer Learning via \ell_1 Regularization

Machine learning algorithms typically require abundant data under a stationary environment. However, environments are nonstationary in many real-world applications. Critical issues lie in how to effectively adapt models under an ever-changing environment. We propose a method for transferring knowledge from a source domain to a target domain via \ell_1 regularization in high dimension. We incorporate \ell_1 regularization of differences between source and target parameters in addition to an ordinary \ell_1 regularization.