The lack of labeled data affects the performance in target domain. Fortunately, there are ample labeled data in some other related source domains. Transfer learning allows knowledge to be transferred from source domains to target domain. In real applications, such as text-image and cross-language transfer learning, the feature spaces of source and target domains are different, that is heterogeneous transfer learning. This paper focuses on heterogeneous transductive transfer learning (HTTL), an approach to improve the performance of unlabeled data in target domain by using some labeled data in heterogeneous source domains. Since the feature spaces of source domains and target domain are different, the key problem is to learn the mapping functions between the heterogeneous source domains and target domain. This paper proposes to learn the mapping functions by unsupervised matching in the different feature spaces. The data in source domains can be re-represented with the mapping functions and transferred to the target domain. Thus, in target domain, there are some labeled data which come from the source domains. Standard machine learning methods such as support vector machine can be used to train classifiers for predicting the labels of unlabeled data in target domain. Moreover, a probabilistic interpretation is derived to verify the robustness of the presented method over certain noises in the utility matrices. A sample complexity bound is given to indicate how many instances are needed to adequately find the mapping functions. The effectiveness of the proposed approach is verified by experiments on four real-world data sets.