计算机工程 ›› 2020, Vol. 46 ›› Issue (2): 1-10.doi: 10.19678/j.issn.1000-3428.0053147

• 热点与综述 • 上一篇    下一篇

基于典型相关分析的多视图降维算法综述

张恩豪a, 陈晓红a, 刘鸿a, 朱玉莲b   

  1. 南京航空航天大学 a. 理学院;b. 信息化技术中心, 南京 211106
  • 收稿日期:2018-11-15 修回日期:2019-01-24 发布日期:2019-02-01
  • 作者简介:张恩豪(1995-),男,硕士研究生,主研方向为机器学习、模式识别;陈晓红,副教授、博士;刘鸿,硕士研究生;朱玉莲,副教授、博士。
  • 基金项目:
    国家自然科学基金(61403193,61703206)。

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

ZHANG Enhaoa, CHEN Xiaohonga, LIU Honga, ZHU Yulianb   

  1. a. College of Science;b. Informationization Technology Center, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
  • Received:2018-11-15 Revised:2019-01-24 Published:2019-02-01

摘要: 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。

关键词: 多视图学习, 典型相关分析, 监督学习, 广义特征值, 降维

Abstract: 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.

Key words: multi-view learning, Canonical Correlation Analysis(CCA), supervised learning, generalized eigenvalue, dimension reduction

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