Manifold Alignment and Distribution Adaptation for Unsupervised Domain Adaptation

Unsupervised domain adaptation is a problem which exploits the knowledge learned from the resource-rich domain to obtain an accurate classifier for the resource-poor domain. Most of the existing methods lift performance by reducing the differences between distributions, such as the difference between marginal probability distributions, the difference between conditional probability distributions, or both. However, all these methods consider the two distributions to be equally important, which could lead to poor classification performance in practical applications. Therefore, a balanced factor is required to weigh the two distributions to compensate for the degraded performance. In this paper, we first introduce this balance factor to weigh the distribution importance. On this base, we utilize the marginal distribution, introduce the ideas of manifold regularization, and then preserve the neighboring structures of the data sets, with the dimension reduction as much as possible. By this way, we propose the manifold alignment and balanced distribution adaptation algorithm. A large number of experiments have also been conducted, showing that our algorithm behaves much better than the previous ones.