As one of the most important forms of psychological behaviors, micro-expression can reveal the real emotion. However, the existing labeled training samples are limited to train a high performance model. To overcome this limit, in this paper we propose a macro-to-micro transformation model which enables to transfer macro-expression learning to micro-expression. Doing so improves the efficiency of the micro-expression features. For this purpose, LBP and LBP-TOP are used to extract macro-expression features and micro-expression features, respectively. Furthermore, feature selection is employed to reduce redundant features. Finally, singular value decomposition is employed to achieve macro-to-micro transformation model. The experimental evaluation based on the incorporated database including CK+ and CASME2 demonstrates that the proposed model achieves a competitive performance compared with the existing micro-expression recognition methods.