Compressive Neighborhood Embedding for Classification

摘要：

　　Recently,spectral manifold learning algorithms on pattern recognition and machine learning orientation have found wide applications.The common strategy for these algorithms,e.g.,Locally Linear Embedding(LLE),facilitates neighborhood relationships which can be constructed by knn or ε criterion.This paper presents a simple technique for constructing the nearest neighborhood by combining e2 and e1 norm.The proposed criterion,called Compressive Neighborhood Embedding(CNE),gives rise to a modified spectral manifold learning technique.The validated discriminating power of sparse representation has illuminated in 1,we additionally formulate the semi-supervised learning variation of CNE,SCNE for short,based on the proposed criterion to utilize both labeled and unlabeled data for inference on a graph.Extensive experiments on semi-supervised classification demonstrate the superiority of the proposed algorithm.

关键词： manifold learning compressive sensing semi-supervised learning

作者: Yuan Chen Zhonglong Zheng

作者单位: Department of Computer Science Zhejiang Normal University Zhejiang,China,321004

会议类型: 国际会议

会议名称: The 2014 10th International Conference on Natural Computation (ICNC 2014) and the 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2014)(第十届自然计算和第十一届模糊系统与知识发现国际会议)

会议地点: 厦门

会议语种:英文

页码: 421-426

在线出版日期: 2014-08-19（万方平台首次上网日期，不代表论文的发表时间）