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.|
|Number of pages||5|
|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
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Conference||44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019|
|Period||5/12/19 → 5/17/19|
Bibliographical noteFunding Information:
Work in this paper was supported in part by NSF grants 1711471, 1514056, and the NIH grant no. 1R01GM104975-01.
© 2019 IEEE.
Copyright 2019 Elsevier B.V., All rights reserved.
- Dimensionality reduction
- Laplacian regularization
- multiview learning
- signal process- ing over graphs