Transfer learning with deep manifold regularized auto-encoders

Abstract

The excellent performance of transfer learning has emerged in the past few years. How to find feature representations which minimize the distance between source and target domains is a crucial problem in transfer learning. Recently, deep learning methods have been proposed to learn higher level and robust representations. However, in traditional methods, label information in source domain is not designed to optimize both feature representations and parameters of the learning model. Additionally, the redundancy of data may incur performance degradation on transfer learning. To address these problems, we propose a novel semi-supervised representation deep learning framework for transfer learning. To obtain this framework, manifold regularization is integrated for the parameter optimization, and the label information is encoded using a softmax regression model in auto-encoders. Meanwhile, whitening layer is introduced to reduce the redundancy of data before auto-encoders. Extensive experiments demonstrate the effectiveness of our proposed framework compared to other competing state-of-the-art baseline methods.