Abstract Heterogeneity of features and lack of correspondence between data points of different domains are the two primary challenges while performing feature transfer. In this paper, we present a novel supervised domain adaptation algorithm (SHDA-RF) that learns the mapping between heterogeneous features of different dimensions. Our algorithm uses the shared label distributions present across the domains as pivots for learning a sparse feature transformation. The shared label distributions and the relationship between the feature spaces and the label distributions are estimated in a supervised manner using random forests. We conduct extensive experiments on three diverse datasets of varying dimensions and sparsity to verify the superiority of the proposed approach over other baseline and state of the art transfer approaches.