Journal
MEMETIC COMPUTING
Volume 8, Issue 2, Pages 85-95Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s12293-016-0188-z
Keywords
Extreme learning machine; Transfer learning (TL); Free sparse representation
Funding
- National Natural Science Foundation of China [61473252, 61375049]
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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.
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