应用于特征提取方法的模糊差分嵌入投影  

Fuzzy Difference Embedding Projection for Feature Extraction

作　　者：万鸣华 Wan Minghua(School of Technology,Nanjing Audit University,Nanjing,211815, China;School of Information Science and Engineering,Southeast University, Nanjing,210096,China)

机构地区：[1]南京审计大学工学院,南京211815 [2]东南大学信息科学与工程学院,南京210096

出　　处：《数据采集与处理》2018年第1期113-121,共9页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(61462064;6177227)资助项目;江苏省自然科学基金(BK20161580;BK20171494)资助项目;中国博士后基金(2016M600674)资助项目

摘　　要：局部线性嵌入(Locally linear embedding,LLE)算法对于分类的结果没有直接的关系。同时,该算法受不同表情、光照以及姿态等因素的影响,识别的效果会大大降低。为了能够很好地解决上述问题,提出基于模糊的差分嵌入投影(Fuzzy difference embedding projection,FDEP)特征提取算法。FDEP算法首先在模糊数学的思想指导下,通过模糊隶属度(Fuzzy sets)的形式表示;然后分别构造模糊局部近邻图与模糊全局方差图来表征局部与全局结构信息,采用最大间距准则函数来构造目标函数避免"小样本"问题;最后,通过拉格朗日乘子解决约束条件下的优化问题。FDEP算法既可以最大化地模糊全局数据之间的非局部散度,又可以保持模糊近邻数据之间的内在联系。在ORL,Yale和AR人脸图像库的实验结果表明,FDEP算法具有较好的识别性能。Locally linear embedding(LLE)algorithm has no direct relationship with the classification.Meanwhile,the recognition effect is decreased when the LLE algorithm is affected by different facial expressions,illumination and pose,etc.,and the distribution of the original sample is usually nonlinear and complex.Therefore,an efficient dimensional reduction and classification algorithm is presented,that is fuzzy difference embedding projection(FDEP)algorithm.The FDEP algorithm constructs different radiograms to characterize the local and the global structure information using fuzzy membership degree(fuzzy sets)under fuzzy thinking,and then uses the maximum margin criterion(MMC)to construct the objective function for avoiding the″small size sample″problem.Finally,the algorithm solves the constrained optimization by Lagrange operators.The FDEP algorithm maintains the original neighbor relations for neighboring data points of the same class and is also crucial to keep away neighboring data points of different classes.The results of face recognition experiments on ORL,Yale and AR face databases demonstrate the effectiveness of the FDEP algorithm.

关 键 词：特征提取 局部线性嵌入 最大间距准则 模糊隶属度 流形 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]