Label Propagation Algorithm Based on Non-negative Sparse Representation

摘要：

Graph-based semi-supervised learning strategy plays an important role in the semi-supervised learning area. This paper presents a novel label propagation algorithm based on nonnegative sparse representation (NSR) for bioinformatics and biometrics. Firstly, we construct a sparse probability graph (SPG) whose nonnegative weight coefficients are derived by nonnegative sparse representation algorithm. The weights of SPG naturally reveal the clustering relationship of labeled and unlabeled samples; meanwhile automatically select appropriate adjacency structure as compared to traditional semi-supervised learning algorithm. Then the labels of unlabeled samples are propagated until algorithm converges. Extensive experimental results on biometrics, UCI machine learning and TDT2 text datasets demonstrate that label propagation algorithm based on NSR outperforms the standard label propagation algorithm.

关键词： biometrics nonnegative sparse representation semi-supervised learning sparse probability graph label propagation

作者: Nanhai Yang Yuanyuan Sang 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: 348-357

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）