Neural Computing and Applications

, Volume 31, Issue 11, pp 7351–7359 | Cite as

Sparse regularized discriminative canonical correlation analysis for multi-view semi-supervised learning

  • Shudong Hou
  • Heng LiuEmail author
  • Quansen Sun
Original Article


For multi-view data representation learning, recently the traditional unsupervised CCA method has been converted to supervised ways by introducing label information from samples. However, such supervised CCA variants require large numbers of labeled samples which hampers its practical application. In this paper, in order to mine the most discriminant information only from a few labeled samples, inspired by sparse representation we propose a novel sparse regularized discriminative CCA method to make use of the label information as much as possible. Through constructing sparse weighted matrices in multiple views, we incorporate the structure information into the original CCA framework to extract fused multi-view features which not only are the most correlated but also carry the important discriminative structure information. Our approach is evaluated on both handwritten dataset and face dataset. The experimental results and the comparisons with other related algorithms demonstrate its effectiveness and superiority.


Canonical correlation analysis Sparse representation Semi-supervised learning Dimension reduction Feature extraction 



This work is partly supported in part by Natural Science Foundation of Anhui Province under Grant 1808085QF210 and Grant 1608085MF129. and in part by the Major and Key Project of Natural Science of Anhui Provincial Department of Education under Grant KJ2015ZD09 and Grant KJ2018A0043.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interest regarding the publication of this paper.


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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyAnhui University of TechnologyMa’anshanPeople’s Republic of China
  2. 2.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingPeople’s Republic of China

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