Canonical Correlation Analysis with Common Graph Priors

Jia Chen, Gang Wang, Yanning Shen, Georgios B Giannakis

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

1 Scopus citations


Canonical correlation analysis (CCA) is a well-appreciated linear subspace method to leverage hidden sources common to two or more datasets. CCA benefits are documented in various applications, such as dimensionality reduction, blind source separation, classification, and data fusion. However, the standard CCA does not exploit the geometry of common sources, which may be deduced from (cross-) correlations, or, inferred from the data. In this context, the prior information provided by the common source is encoded here through a graph, and is employed as a CCA regularizer. This leads to what is termed here as graph CCA (gCCA), which accounts for the graph-induced knowledge of common sources, while maximizing the linear correlation between the canonical variables. When the dimensionality of data vectors is high relative to the number of vectors, the dual formulation of the novel gCCA is also developed. Tests on two real datasets for facial image classification showcase the merits of the proposed approaches relative to their competing alternatives.

Original languageEnglish (US)
Title of host publication2018 IEEE Statistical Signal Processing Workshop, SSP 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781538615706
StatePublished - Aug 29 2018
Event20th IEEE Statistical Signal Processing Workshop, SSP 2018 - Freiburg im Breisgau, Germany
Duration: Jun 10 2018Jun 13 2018

Publication series

Name2018 IEEE Statistical Signal Processing Workshop, SSP 2018


Other20th IEEE Statistical Signal Processing Workshop, SSP 2018
CityFreiburg im Breisgau

Bibliographical note

Funding Information:
This work was supported by NSF grants 1500713, 1514056, and the NIH grant no. 1R01GM104975-01.


  • Canonical correlations
  • dimensionality reduction
  • generalized eigenvalue
  • signal processing over graphs

Fingerprint Dive into the research topics of 'Canonical Correlation Analysis with Common Graph Priors'. Together they form a unique fingerprint.

Cite this