Transfer learning with stacked reconstruction independent component analysis

Abstract

Significant improvements to transfer learning have emerged in recent years, because deep learning has been proposed to learn more higher level and robust features. However, most of existing deep learning approaches are based on the framework of auto-encoder or sparse auto-encoder, which pose challenges for independent component analysis and fail to measure similarities between data spaces. Therefore, in this paper, we propose a new strategy to achieve a better feature representation performance for transfer learning. There are several advantages in our method as follows: 1) The model of Stacked Reconstruction Independent Component Analysis (SRICA) is used to pursuit an optimal feature representation; 2) The label information is used by Logistic Regression Model to optimize representation features and the distance of distributions between domains is minimized by the method of KL-Divergence. Extensive experiments conducted on several image datasets demonstrate the superiority of our proposed method compared with all competing state-of-the-art methods.