基于超图正则化的概念分解及在数据表示中的应用  

Hyper-graph regularized concept factorization algorithm and its application to data representation

作　　者：李雪[1] 赵春霞[1] 舒振球[1] 郭剑辉[1] 

机构地区：[1]南京理工大学计算机科学与工程学院,南京210094

出　　处：《控制与决策》2015年第8期1399-1404,共6页Control and Decision

基　　金：国家自然科学基金项目(61272220;61101197;90820306);中国博士后科学基金项目(2014M551599);江苏省社会安全图像与视频理解重点实验室基金项目(30920130122006);江苏省普通高校研究生科研创新计划项目(KYLX 0383)

摘　　要：针对传统图模型的流形学习无法准确表达数据间多元几何结构信息的问题,提出一种基于超图正则化的概念分解(HRCF)算法.该算法用一组具有相似属性的数据子集构建超边,建立数据间高阶关系的超图模型.通过在概念分解算法中增加超图正则项,保持数据间多元几何流形结构,提高了算法的鉴别性.在Yale库、USPS库和TDT2库上的实验表明,HRCF算法明显提高了聚类的准确率和归一化互信息,验证了算法的有效性.The manifold learning methods of the simple graph model ignored the high-order relationship between data points. Therefore, an algorithm, called hyper-graph regularized concept factorization（HRCF） is proposed. HRCF considers the high-order relationship of samples by constructing the hyper-edge in hyper-graph with a subset of data points sharing with some attribute. The concept factorization（CF） algorithm can preserve the high-order relationship of the manifold structure,by adding hyper-graph regulation term in clustering. Thus, the algorithm has more discrimination power. The experimental results on Yale, USPS and TDT2 database show that the proposed approach provides a better representation and achieves better clustering results in terms of accuracy and normalized mutual information, and verify the effectiveness of the proposed method.

关 键 词：概念分解 流形正则项 非负矩阵分解 聚类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]