TY - GEN
T1 - Multiview Canonical Correlation Analysis over Graphs
AU - Chen, Jia
AU - Wang, Gang
AU - Giannakis, Georgios B.
N1 - Publisher Copyright:
© 2019 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85065016999&partnerID=8YFLogxK
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 -
