Extreme learning machine via free sparse transfer representation optimization

Xiaodong Li, Weijie Mao, Wei Jiang, Ye Yao

Abstract: In this paper, we propose a general framework for Extreme Learning Machine via free sparse transfer representation, which is referred to as transfer free sparse representation based on extreme learning machine (TFSR-ELM). This framework is suitable for different assumptions related to the divergence measures of the data distributions, such as a maximum mean discrepancy and K-L divergence. We propose an effective sparse regularization for the proposed free transfer representation learning framework, which can decrease the time and space cost. Different solutions to the problems based on the different distribution distance estimation criteria and convergence analysis are given. Comprehensive experiments show that TFSR-based algorithms outperform the existing transfer learning methods and are robust to different sizes of training data.