Applied Intelligence

, Volume 49, Issue 2, pp 650–660 | Cite as

Multi-view uncorrelated discriminant analysis via dependence maximization

  • Xin ShuEmail author
  • Peisen Yuan
  • Haiyan Jiang
  • Darong Lai


This paper proposes a novel multi-view discriminant analysis based on Hilbert-Schmidt Independence Criterion (HSIC) and canonical correlation analysis (CCA). We use HSIC to identify a lower dimensional discriminant common subspace in which the dependence between multi-view features and the associated labels is maximized. CCA is utilized to achieve maximum correlation between different views in the common subspace. Motivated by the successful application of uncorrelated discriminant analysis, we further extend our approach to extract features with minimum redundancy. Experimental results validate the effectiveness of our proposed approaches.


Hilbert-Schimdt independence criterion Feature extraction Multi-view discriminant analysis Canonical correlation analysis 



This work was supported by the Natural Science Foundation of China (Grants NO.61602248), the Natural Science Foundation of Jiangsu Province (Grants No. BK20160741) and the Fundamental Research Funds for the Central Universities (No.KJQN201733).


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information Science and TechnologyNanjing Agricultural UniversityNanjingChina
  2. 2.School of Computer Science and EngineeringSoutheast UniversityNanjingChina

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