基于典型相关分析的多视图学习方法综述

郭陈凤; 伍冬睿

doi:10.11959/j.issn.2096-6652.202206

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基于典型相关分析的多视图学习方法综述

综述与展望 | 更新时间：2024-06-05

- 基于典型相关分析的多视图学习方法综述
- A survey on canonical correlation analysis based multi-view learning
- 智能科学与技术学报 2022年4卷第1期页码：14-26
- 作者机构：
- 作者简介：
  
  [ "郭陈凤（1995− ），女，华中科技大学人工智能与自动化学院硕士生，主要研究方向为脑机接口、机器学习" ]
  [ "伍冬睿（1982− ），男，博士，华中科技大学人工智能与自动化学院教授，主要研究方向为机器学习、脑机接口、计算智能、情感计算" ]
- 基金信息：
  
  武汉市应用基础前沿项目;Wuhan Science and Technology Bureau(2020020601012240);湖北省技术创新专项;Technology Innovation Project of Hubei Province of China(2019AEA171)
- DOI：10.11959/j.issn.2096-6652.202206
  中图分类号： TP181
- 网络出版日期：2022-03，
  
  纸质出版日期：2022-03-15
- 稿件说明：
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郭陈凤, 伍冬睿. 基于典型相关分析的多视图学习方法综述[J]. 智能科学与技术学报, 2022,4(1):14-26.

Chenfeng GUO, Dongrui WU. A survey on canonical correlation analysis based multi-view learning[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 14-26.
郭陈凤, 伍冬睿. 基于典型相关分析的多视图学习方法综述[J]. 智能科学与技术学报, 2022,4(1):14-26. DOI： 10.11959/j.issn.2096-6652.202206.

Chenfeng GUO, Dongrui WU. A survey on canonical correlation analysis based multi-view learning[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 14-26. DOI： 10.11959/j.issn.2096-6652.202206.

摘要

多视图学习是将不同来源的特征子集加以融合的策略。典型相关分析是多视图学习中的重要方法，旨在最大化不同视图之间的相关性。传统的典型相关分析仅能计算两个视图之间的线性相关性，无法应用于包含多个视图或包含非线性相关性的数据集。此外，如果将典型相关分析应用于有监督任务，其作为一种无监督方法将导致标签信息的浪费。针对上述问题，提出大量非线性的、针对多个视图的、有监督的基于典型相关分析的多视图学习方法。首先，概述经典的基于典型相关分析的多视图方法；然后介绍这些方法在模式识别、跨模态检索和分类，以及多视图嵌入中的典型应用；最后，对基于典型相关分析的多视图学习方法面临的挑战和未来研究方向进行了总结和展望。

Abstract

Multi-view learning (MVL) is a strategy for fusing data from different sources or subsets.Canonical correlation analysis (CCA) is very important in MVL

whose main idea is to maximize the correlation of different views.The traditional CCA can only calculate the linear correlation between two views.Moreover

it is unsupervised

and the label information is wasted in supervised learning tasks.Many nonlinear

supervised

or generalized extensions have been proposed to accommodate these limitations.Firstly

a comprehensive overview of representative CCA approaches was provided.Then their classical applications in pattern recognition

cross-modal retrieval and classification

and multi-view embedding were described.Finally

the challenges and future research directions of CCA-based MVL approaches were pointed out.

关键词

Keywords

references

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