Chinese Journal of Intelligent Science and Technology ›› 2022, Vol. 4 ›› Issue (1): 14-26.doi: 10.11959/j.issn.2096-6652.202206

• Surveys and Prospectives • Previous Articles     Next Articles

A survey on canonical correlation analysis based multi-view learning

Chenfeng GUO, Dongrui WU   

  1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
  • Revised:2021-06-18 Online:2022-03-15 Published:2022-03-01
  • Supported by:
    Wuhan Science and Technology Bureau(2020020601012240);Technology Innovation Project of Hubei Province of China(2019AEA171)

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.

Key words: canonical correlation analysis, multi-view learning, multi-modal retrieval, multi-view embedding

CLC Number: 

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