Robust Semi-supervised Learning for Biometrics

摘要：

To deal with the problem of sensitivity to noise in semi-supervised learning for biometrics, this paper proposes a robust Gaussian-Laplacian Regularized (GLR) framework based on maximum correntropy criterion (MCC), called GLR-MCC, along with its convergence analysis. The half quadratic (HQ) optimization technique is used to simplify the correntropy optimization problem to a standard semisupervised problem in each iteration. Experimental results show that the proposed GRL-MCC can effectively improve the semi-supervised learning performance and is robust to mislabeling noise and occlusion as compared with GLR.

关键词： Biometrics Semi-supervised Learning Robust Correntropy

作者: Nanhai Yang Mingming Huang Ran He Xiukun Wang

作者单位: Department of Computer Science and Technology,Dalian University of Technology,116024 Dalian, China

会议类型: 国际会议

会议名称: International Conference on Life System Modeling and Simulation,and International Conference on Intelligent Computing for Sustainable Energy and Environment(2010生命系统建模与仿真国际会议暨m2010可持续能源与环境智能计算国际会议)

会议地点: 无锡

会议语种:英文

页码: 466-476

在线出版日期: 2010-09-17（万方平台首次上网日期，不代表论文的发表时间）