一种多局部线性模式保持的降维算法  

A DIMENSION REDUCTION ALGORITHM FOR MULTI LOCAL LINEAR PATTERN PRESERVING

作　　者：王红娟[1] 胡海根[2] Wang Hongjuan;Hu Haigen(Henan Vocational College of Agriculture,Zhengzhou 451450,Henan,China;College of Computer Science&Technology,Zhejiang University of Technology,Hangzhou 310024,Zhejiang,China)

机构地区：[1]河南农业职业学院,河南郑州451450 [2]浙江工业大学计算机科学与技术学院,浙江杭州310024

出　　处：《计算机应用与软件》2024年第8期334-344,共11页Computer Applications and Software

基　　金：浙江省自然科学基金项目(LY18F030025);河南省职业技术教育学会2020年度研究课题(2020-ZJXH-005)。

摘　　要：为了更准确地捕捉数据的局部非线性结构,提出一种基于多局部线性模式保持的降维算法。该文通过局部区域线性重构相应的数据点,利用方向导数代替一阶泰勒展开式中的梯度,降低逼近误差;利用多重线性模式表征数据点,从而更精确地描述数据的局部非线性几何特征;进一步通过最小化嵌入数据空间中的多局部线性重构误差得到嵌入结果。在4个合成数据集和6个真实数据集上实验,结果表明提出方法能够准确捕捉数据的多个非线性结构。In order to capture the local nonlinear structure of data more accurately,an unsupervised dimension reduction algorithm based on multiple local linear pattern preserving is proposed.The corresponding data points were reconstructed linearly in the local region,and the directional derivative was used to replace the gradient in the first-order Taylor expansion to reduce the approximation error.The multi-linear patterns were used to represent the data points,so as to describe the local nonlinear geometric features of the data more accurately.Furthermore,the embedding result was obtained by minimizing the multi local linear reconstruction error in the embedded data space.The experimental results on 4 synthetic datasets and 6 real datasets show that the proposed method can accurately capture the nonlinear structure.

关 键 词：局部非线性 无监督 维数降维 线性重构 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]