Large-scale regularized sumcor GCCA via penalty-dual decomposition

Charilaos I. Kanatsoulis, Xiao Fu, Nicholas D. Sidiropoulos, Mingyi Hong

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

1 Scopus citations


The sum-of-correlations (SUMCOR) generalized canonical correlation analysis (GCCA) aims at producing low-dimensional representations of multiview data via enforcing pairwise similarity of the reduced-dimension views. SUMCOR has been applied to a large variety of applications including blind separation, multilingual word embedding, and cross-modality retrieval. Despite the NP-hardness of SUMCOR, recent work has proposed effective algorithms for handling it at very large scale. However, the existing scalable algorithms are not easy to extend to incorporate structural regularization and prior information - which are critical for real-world applications where outliers and modeling mismatches are present. In this work, we propose a new computational framework for large-scale SUMCOR GCCA. The algorithm can easily incorporate a suite of structural regularizers which are frequently used in data analytics, has lightweight updates and low memory complexity, and can be easily implemented in a parallel fashion. The proposed algorithm is also guaranteed to converge to a Karush-Kuhn-Tucker (KKT) point of the regularized SUMCOR problem. Carefully designed simulations are employed to demonstrate the effectiveness of the proposed algorithm.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Print)9781538646588
StatePublished - Sep 10 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: Apr 15 2018Apr 20 2018

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149


Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018

Bibliographical note

Funding Information:
This work was supported in part by National Science Foundation under Project NSF ECCS-1608961 and Project NSF IIS-1447788.

Publisher Copyright:
© 2018 IEEE.


  • Feature extraction
  • Generalized canonical correlation analysis
  • Multi-view analysis
  • Regularization


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