基于典型相关分析的多视图降维算法综述

Overview of Multi-view Dimension Reduction Algorithm Based on Canonical Correlation Analysis

随着数据采集技术的发展,人们获取数据的途径呈多样化,所得到的数据往往具有多个视图,从而形成多视图数据。利用多视图数据不同的信息特征,设计相应的多视图学习策略以提高分类器的性能是多视图学习的研究目标。为更好地利用多视图数据,促进降维算法在实际中的应用,对多视图降维算法进行研究。分析多视图数据和多视图学习,在典型相关分析(CCA)的基础上追溯多视图CCA和核CCA,介绍多视图降维算法从两个视图到多个视图以及从线性到非线性的演化过程,总结各种融入判别信息和近邻信息的多视图降维算法,以更好地学习多视图降维算法。在此基础上,对比分析多视图降维算法的特点及存在的问题,并对未来的研究方向进行展望。

With the development of data acquisition technology and the diversification of data obtaining approaches,the obtained data often have multiple views,so the multi-view data are formed.To study the information contained in these data becomes an research objective of multi-view learning.In order to make better use of multi-view data and improve the practical application of dimension reduction algorithms,this paper conducts a research on multi-view dimension reduction algorithms.This paper first reviews multi-view data and multi-view learning,and then,on the basis of Canonical Correlation Analysis(CCA),MCCA and KCCA are reviewed as well.Moreover,the evolution of multi-view dimension reduction algorithms,from two-view data to multi-view data and from linear to nonlinear is introduced herein.Then,this paper further summarizes various multi-view dimension algorithms integrating discriminant information and nearest neighbor information,so as to have a better understanding of these algorithms.Finally,this paper analyzes the characteristics and drawbacks of the multi-view dimension reduction algorithms and proposes future research directions.