A Transfer Learning Algorithm Based on Regularized Discriminant Analysis - WANG Li-li; FENG Qi-shuai; CHEN De-yun; YANG Hai-lu (Journal of Harbin University of Science and Technology)
Aiming at the problem that most instance-based transfer learning methods are difficult to estimate the distribution parameters and having poor generalization ability, a regularized discriminant transfer learning algorithm is proposed. Based on the discriminant analysis and semi-supervised learning theory, the semi-supervised Gauss kernel discriminant analysis method is studied by kernel method and regularization method, and the reusable samples are transferred by constructing the revised embedding space. On the one hand, screening samples in the mapping space can solve the difficulty of estimating the parameters of domain distribution; on the other hand, introducing pseudo-labeled data and defining the distance function can avoid over-fitting problems. The experimental results on text and non-text datasets validate that the proposed algorithm can effectively improve the accuracy and generalization ability of transferring.