Deep adversarial canonical correlation analysis

Wenqi Fan, Yao Ma, Han Xu, Xiaorui Liu, Jianping Wang, Qing Li, Jiliang Tang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations


Canonical Correlation Analysis (CCA) aims to learn the linear projections of two sets of variables where they are correlated maximally, which is not optimal for variables with non-linear relations. Recent years have witnessed great efforts in developing deep neural networks based CCA models, which are able to learn flexible non-linear and highly correlated representations between two variables. In addition to learning representations, generating realistic multi-view samples is also becoming highly desired in many real-world applications. However, the majority of existing CCA models do not provide mechanisms for realistic samples generation. Meanwhile, adversarial learning techniques such as generative adversarial networks have been proven to be effective in generating realistic samples similar to real data distribution. Thus, incorporating adversarial learning techniques has a great potential to advance Canonical Correlation Analysis. In this paper, we harness the power of adversarial learning techniques to equip Canonical Correlation Analysis with the ability of realistic data generation. In particular, we propose a Deep Adversarial Canonical Correlation Analysis model (DACCA), which can simultaneously learn representation of multi-view data but also generate realistic multi-view samples. Comprehensive experiments have been conducted on three real-world datasets and the results demonstrate the effectiveness of the proposed model. Our code is available at

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020
EditorsCarlotta Demeniconi, Nitesh Chawla
PublisherSociety for Industrial and Applied Mathematics Publications
Number of pages9
ISBN (Electronic)9781611976236
StatePublished - 2020
Externally publishedYes
Event2020 SIAM International Conference on Data Mining, SDM 2020 - Cincinnati, United States
Duration: May 7 2020May 9 2020

Publication series

NameProceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020


Conference2020 SIAM International Conference on Data Mining, SDM 2020
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software


  • Canonical Correlation Analysis (CCA)
  • Generative Adversarial Network (GAN)
  • Representation Learning


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