基于判别最小二乘回归的局部保留子空间学习算法  

Subspace learning algorithm based on discriminant least squares regression locality preserving projection

作　　者：杨旭 杨丹[1] 周小雯 张小洪[1] Yang Xu;Yang Dan;Zhou Xiaowen;Zhang Xiaohong(School of Software,Chongqing University,Chongqing 400030,China)

机构地区：[1]重庆大学软件学院,重庆400030

出　　处：《计算机应用研究》2018年第10期3134-3137,共4页Application Research of Computers

基　　金：国家自然科学基金青年基金资助项目(61602068);重庆市科技计划项目基础科学与前沿技术研究专项一般项目(cstc2016jcyj A0458)

摘　　要：针对稀疏表示能够有效提取出相同类内部样本间和不同类之间的稀疏性,却具有高复杂度求解过程和存在丢失数据结构关键信息的缺点进行了研究,提出了判别最小二乘局部保持投影(DLSLPP)算法。DLSLPP算法利用最小二乘回归充分而完整地提取了数据之间的结构信息,同时利用最小二乘回归具有数值解的优势降低了算法的复杂度。此外,DLSLPP算法采用不同于传统算法的新型加权平均方式构造数据代表样本,增强了算法的判别能力。在四个图像分类数据集和四种算法上的对比实验表明DLSLPP算法能够达到十分优异的效果。Sparse representation(SR)can encourage sparsity between the within-cluster and between-cluster.But it would be highly complex in solution and may miss the important correlation structure in the data.In order to overcome those disadvantages,this paper proposed a novel subspace algorithm discriminant least squares locality preserving projection(DLSLPP).DLSLPP used least squares regression(LSR)to catch the complete incident matrix of data.DLSLPP had low complex since it could get numerical solution.In particular,DLSLPP utilized weighted-mean method to construct representative samples different from traditional methods,thus could increase the discriminant power for classification.Experiments on four image database and several algorithms show that DLSLPP can achieve a promising performance.

关 键 词：数据降维 最小二乘回归 特征提取 子空间学习 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]