Domain adaptation in machine learning and image processing aims to benefit from gained knowledge of the multiple labeled training sets (i.e. source domain) to classify the unseen test set (i.e. target domain). Therefore, the major issue emerges from dataset bias where the source and target domains have different distributions. In this paper, we introduce a novel unsupervised domain adaptation method for cross-domain visual classification. We suggest a unified framework that reduces both statistically and geometrically shift across domains, referred to as Transferred Local Fisher Discriminant Analysis (TLFDA). Specifically, TLFDA projects data into a shared subspace to minimize the distribution shift between domains and simultaneously preserves the discrimination across different classes. TLFDA maximizes the between-class separability and preserves the within-class local structure in form of an objective function metrics. The objective function is solved effectively in closed-form. Broad experiments demonstrate that TLFDA significantly outperforms many state-of-the-art domain adaptation methods on different cross-domain visual classification tasks.