Multi-Class Learning using Unlabeled Samples: Theory and Algorithm

In this paper, we investigate the generalization performance of multi-class classification, for which we obtain a shaper error bound by using the notion of local Rademacher complexity and additional unlabeled samples, substantially improving the state-of-the-art bounds in existing multi-class learning methods. The statistical learning motivates us to devise an efficient multi-class learning framework with the local Rademacher complexity and Laplacian regularization. Coinciding with the theoretical analysis, experimental results demonstrate that the stated approach achieves better performance.

Authors

• Jian Li
• Yong Liu
• Rong Yin Institute of Information Engineering, CAS School of Cyber Security, University of Chinese Academy of Sciences
• Weiping Wang Institute of Information Engineering, Chinese Academy of Sciences

Keywords

• Machine Learning: Classification
• Machine Learning: Learning Theory
• Machine Learning: Semi-Supervised Learning