||Transfer learning is a new machine learning and data mining framework that allows the training and test data to come from different distributions and/or feature spaces. We can find many novel applications of machine learning and data mining where transfer learning is helpful, especially when we have limited labeled data in our domain of interest. In this thesis, we first survey different settings and approaches of transfer learning and give a big picture of the field. We focus on latent space learning for transfer learning, which aims at discovering a “good” common feature space across domain, such that knowledge transfer becomes possible. In our study, we propose a novel dimensionality reduction framework for transfer learning, which tries to reduce the distance between different domains while preserve data properties as much as possible. This framework is general for many transfer learning problems when domain knowledge is unavailable. Based on this framework, we propose three effective solutions to learn the latent space for transfer learning. We apply these methods to two diverse applications: cross-domain WiFi localization and cross-domain text classification, and achieve promising results. Furthermore, for a specific application area, such as sentiment classification, where domain knowledge is available for encoding to transfer learning methods, we propose a spectral feature alignment algorithm for cross-domain learning. In this algorithm, we try to align domain-specific features from different domains by using some domain independent features as a bridge. Experimental results show that this method outperforms a state-of-the-art algorithm in two real-world datasets on cross-domain sentiment classification.