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) |
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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 | |
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 |
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Volume | 2019-May |
ISSN (Print) | 1520-6149 |
Conference
Conference | 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 |
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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