Fuzzy Inference and Manifold Regularization Combined Feature Transfer Learning

Abstract

Transfer learning leverages the rich data in the source domain to provide support for building accurate models in the target domain. Feature transfer learning is a kind of widely studied technology in transfer learning, but the existing feature transfer methods are facing with the following problems. Firstly, some existing methods can only implement linear feature transfer learning, so the ability of these methods to transfer learning is limited. Secondly, other kinds of methods can achieve nonlinear feature transfer learning, while it is often necessary to introduce strategies such as kernel techniques, which makes the process of feature transfer difficult to understand. In view of these problems, this paper introduces fuzzy reasoning technology and proposes a feature transfer method based on uncertain reasoning rules. The proposed method uses fuzzy inference system to realize feature transfer and uses manifold regularization to avoid information loss during feature transfer learning. Because the fuzzy system has good nonlinear modeling ability and good interpretation, the proposed method has good nonlinear feature transfer ability and is easy to understand the obtained new features. A large number of experiments have proven that the proposed method can be significantly better than the existing methods in the cross-domain image classification problem.

Keywords

• feature transfer
• nonlinear model
• kernel technique
• fuzzy system
• interpretability