Manifold Transfer Learning via Discriminant Regression Analysis

In transfer learning, how to effectively transfer useful information from the source domain to the target domain is crucial. In this paper, we propose a novel transfer learning method for image classification, named manifold transfer learning via discriminant regression analysis (MTL-DRA), to transfer the local geometry structure information from the source domain to the target domain and ensure that the transform matrix is robust or sparse so that samples from different domains can be well combined. In MTL-DRA, we encode discriminant information of the source domain to the target domain by introducing between- and within-class graphs to preserve within-class similarity and reduce between-class similarity. With different norms as constraints, MTL-DRA overcomes the disturbance of noise and avoids negative transfer learning. To improve the robustness of MTL-DRA, we encode a nuclear norm constraint and propose robust MTL-DRA (RMTL-DRA). We analyzed the convergence and complexity of the two proposed methods. To verify the performance of the proposed methods, we conducted extensive experiments on five public image benchmarks. The experimental results show that the proposed methods outperform state-of-the-art transfer learning methods.