基于谱聚类特征向量分析的模态划分方法  

Mode Partitioning Method Based on Eigenvector Analysis in Spectral Clustering

作　　者：南男 杨健 赵晶晶 侍洪波 

机构地区：[1]华东理工大学化工过程先进控制和优化技术教育部重点实验室,上海200237

出　　处：《华东理工大学学报（自然科学版）》2017年第5期669-676,共8页Journal of East China University of Science and Technology

基　　金：国家自然科学基金(61374140;61673173)

摘　　要：在实际生产过程中,过程数据的多模态特性会对数据建模产生一定的影响,进行模态划分有利于获取精确的模型。目前常用的模态划分方法,如k-means、c-means等聚类方法,在有过渡过程的模态划分应用中,有时不能得到理想的结果。本文提出了一种通用的模态划分方法,以谱聚类算法中相似矩阵的特征向量分析为基础,基于相似矩阵的特征向量与其所包含的聚类信息的关系,使用高斯曼哈顿距离构造模态标签,并用小窗口思想实现动态多模态过程的模态划分。通过对稳态与带过渡过程的多模态数据的实验验证了该算法的有效性。The multimode characteristics of the process data in actual production process will have acertain impact on the data modeling. Moreover,k-means,c-means and other clustering are several commonly used methods on mode analysis. However, these algorithms maynot perform well the transition process. In this work, a general mode division method is proposed, in which the spectralclustering analysis of the similarity matrix is utilized. Moreover, by means of the relationship between theeigenvector of the similarity matrix and the involved classification information,a Gauss Manhattan distance is constructed for indicator variable such that the mode partitioning is achieved via the small window.Finally,the effectiveness of the proposed algorithm is verified by the experiment of multimode data withtransition and nontransition process.

关 键 词：多模态数据 模态划分 过渡过程 谱聚类 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]