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Multiview Canonical Correlation Analysis over Graphs

Published on May 1, 2019 in ICASSP (International Conference on Acoustics, Speech, and Signal Processing)
· DOI :10.1109/ICASSP.2019.8683096
Jia Chen3
Estimated H-index: 3
(UMN: University of Minnesota),
Gang Wang8
Estimated H-index: 8
(UMN: University of Minnesota),
Georgios B. Giannakis112
Estimated H-index: 112
(UMN: University of Minnesota)
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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.
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Published on Jun 1, 2019in IEEE Transactions on Signal Processing 5.23
Jia Chen3
Estimated H-index: 3
(UMN: University of Minnesota),
Gang Wang8
Estimated H-index: 8
(UMN: University of Minnesota),
Georgios B. Giannakis112
Estimated H-index: 112
(UMN: University of Minnesota)
Multiview canonical correlation analysis (MCCA) seeks latent low-dimensional representations encountered with multiview data of shared entities (a.k.a. common sources). However, existing MCCA approaches do not exploit the geometry of the common sources, which may be available a priori , or can be constructed using certain domain knowledge. This prior information about the common sources can be encoded by a graph, and be invoked as a regularizer to enrich the maximum variance MCCA framework. In t...