Fast and Stable Coupled Minor Component Analysis Rules

摘要：

　　Coupled learning algorithm,in which the eigenvector and eigenvalue of a covariance matrix are estimated in coupled equations simultaneously,is a solution to the speed-stability problem that plagues most noncoupled learning rules.M(o)ller has proposed a class of well-performed CPCA (coupled principal component analysis) algorithms,but it is a pity that only few of CMCA (coupled minor component analysis) algorithm was proposed until now.In this paper,to expand the CMCA field,we propose some stable CMCA algorithms based on M(o)llers CPCA and CMCA algorithms.The proposed algorithms provide efficient methods to extract the minor eigenvector and eigenvalue of a covariance matrix.Simulation experiments confirm the effectiveness of the proposed algorithms.

关键词： neural networks coupled algorithms minor component analysis (MCA) eigenvector and eigenvalue

作者: Xiaowei Feng Hongguang Ma Xiangyu Kong Caixing Zhang

作者单位: Xian Research Institute of High Technology Xian 710025, China

会议类型: 国际会议

会议名称: 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference(IAEAC 2015)(2015 IEEE先进信息技术,电子与自动化控制国际会议)

会议地点: 重庆

会议语种:英文

页码: 54-59

在线出版日期: 2015-12-19（万方平台首次上网日期，不代表论文的发表时间）