Multiview Canonical Correlation Analysis over Graphs

Jia Chen; Gang Wang; Georgios B Giannakis

doi:10.1109/ICASSP.2019.8683096

Multiview Canonical Correlation Analysis over Graphs

Jia Chen
, Gang Wang
, Georgios B Giannakis

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

Multiview canonical correlation analysis (MCCA) looks for shared low-dimensional representations hidden in multiple transformations of common source signals. Existing MCCA approaches do not exploit the geometry of common sources, which can be either given a priori, or constructed from do- main knowledge. In this paper, a novel graph-regularized (G) MCCA is developed to account for such geometry-bearing in- formation via graph regularization in the classical maximum- variance MCCA model. GMCCA minimizes the distance between the sought canonical variables and the common sources, while incorporating the graph-induced prior of these sources. To capture nonlinear dependencies, GMCCA is fur- ther broadened to the graph-regularized kernel (GK) MCCA. Numerical tests using real datasets document the merits of G(K)MCCA in comparison with competing alternatives.

Original language	English (US)
Title of host publication	2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2947-2951
Number of pages	5
ISBN (Electronic)	9781479981311
DOIs	https://doi.org/10.1109/ICASSP.2019.8683096
State	Published - May 2019
Event	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom Duration: May 12 2019 → May 17 2019

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2019-May
ISSN (Print)	1520-6149

Conference

Conference	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/Territory	United Kingdom
City	Brighton
Period	5/12/19 → 5/17/19

Bibliographical note

Funding Information:
Work in this paper was supported in part by NSF grants 1711471, 1514056, and the NIH grant no. 1R01GM104975-01.

Publisher Copyright:
© 2019 IEEE.

Keywords

Dimensionality reduction
Laplacian regularization
multiview learning
signal process- ing over graphs

Publisher link

10.1109/ICASSP.2019.8683096

Find It

Find It at U of M Libraries

Cite this

Chen, J., Wang, G., & Giannakis, G. B. (2019). Multiview Canonical Correlation Analysis over Graphs. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 2947-2951). Article 8683096 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8683096

Multiview Canonical Correlation Analysis over Graphs. / Chen, Jia; Wang, Gang; Giannakis, Georgios B.
2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2947-2951 8683096 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, J, Wang, G & Giannakis, GB 2019, Multiview Canonical Correlation Analysis over Graphs. in 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings., 8683096, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2019-May, Institute of Electrical and Electronics Engineers Inc., pp. 2947-2951, 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, Brighton, United Kingdom, 5/12/19. https://doi.org/10.1109/ICASSP.2019.8683096

Chen J, Wang G, Giannakis GB. Multiview Canonical Correlation Analysis over Graphs. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2947-2951. 8683096. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2019.8683096

Chen, Jia ; Wang, Gang ; Giannakis, Georgios B. / Multiview Canonical Correlation Analysis over Graphs. 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2947-2951 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

@inproceedings{f25c50f0699746c0a90d06f1a16baf81,

title = "Multiview Canonical Correlation Analysis over Graphs",

abstract = "Multiview canonical correlation analysis (MCCA) looks for shared low-dimensional representations hidden in multiple transformations of common source signals. Existing MCCA approaches do not exploit the geometry of common sources, which can be either given a priori, or constructed from do- main knowledge. In this paper, a novel graph-regularized (G) MCCA is developed to account for such geometry-bearing in- formation via graph regularization in the classical maximum- variance MCCA model. GMCCA minimizes the distance between the sought canonical variables and the common sources, while incorporating the graph-induced prior of these sources. To capture nonlinear dependencies, GMCCA is fur- ther broadened to the graph-regularized kernel (GK) MCCA. Numerical tests using real datasets document the merits of G(K)MCCA in comparison with competing alternatives.",

keywords = "Dimensionality reduction, Laplacian regularization, multiview learning, signal process- ing over graphs",

author = "Jia Chen and Gang Wang and Giannakis, \{Georgios B\}",

note = "Funding Information: Work in this paper was supported in part by NSF grants 1711471, 1514056, and the NIH grant no. 1R01GM104975-01. Publisher Copyright: {\textcopyright} 2019 IEEE.; 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",

year = "2019",

month = may,

doi = "10.1109/ICASSP.2019.8683096",

language = "English (US)",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2947--2951",

booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",

}

TY - GEN

T1 - Multiview Canonical Correlation Analysis over Graphs

AU - Chen, Jia

AU - Wang, Gang

AU - Giannakis, Georgios B

PY - 2019/5

Y1 - 2019/5

N2 - Multiview canonical correlation analysis (MCCA) looks for shared low-dimensional representations hidden in multiple transformations of common source signals. Existing MCCA approaches do not exploit the geometry of common sources, which can be either given a priori, or constructed from do- main knowledge. In this paper, a novel graph-regularized (G) MCCA is developed to account for such geometry-bearing in- formation via graph regularization in the classical maximum- variance MCCA model. GMCCA minimizes the distance between the sought canonical variables and the common sources, while incorporating the graph-induced prior of these sources. To capture nonlinear dependencies, GMCCA is fur- ther broadened to the graph-regularized kernel (GK) MCCA. Numerical tests using real datasets document the merits of G(K)MCCA in comparison with competing alternatives.

AB - Multiview canonical correlation analysis (MCCA) looks for shared low-dimensional representations hidden in multiple transformations of common source signals. Existing MCCA approaches do not exploit the geometry of common sources, which can be either given a priori, or constructed from do- main knowledge. In this paper, a novel graph-regularized (G) MCCA is developed to account for such geometry-bearing in- formation via graph regularization in the classical maximum- variance MCCA model. GMCCA minimizes the distance between the sought canonical variables and the common sources, while incorporating the graph-induced prior of these sources. To capture nonlinear dependencies, GMCCA is fur- ther broadened to the graph-regularized kernel (GK) MCCA. Numerical tests using real datasets document the merits of G(K)MCCA in comparison with competing alternatives.

KW - Dimensionality reduction

KW - Laplacian regularization

KW - multiview learning

KW - signal process- ing over graphs

UR - https://www.scopus.com/pages/publications/85065016999

UR - https://www.scopus.com/pages/publications/85065016999#tab=citedBy

U2 - 10.1109/ICASSP.2019.8683096

DO - 10.1109/ICASSP.2019.8683096

M3 - Conference contribution

AN - SCOPUS:85065016999

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 2947

EP - 2951

BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019

Y2 - 12 May 2019 through 17 May 2019

ER -

Multiview Canonical Correlation Analysis over Graphs

Abstract

Publication series

Conference

Bibliographical note

Keywords

Publisher link

Find It

Other files and links

Fingerprint

Cite this