Abstract
Canonical correlation analysis (CCA) and its nonlinear extensions have shown promising performance in multi-view representation learning. One of the most representative methods is Deep Canonical Correlation Analysis Autoencoders (DCCAE), which combines CCA with a reconstruction loss to reserve more information in representations. However, the contradiction between reconstruction and correlation maximization hinders the optimization of them. Here we propose a multi-view representation learning method named Full Reconstruction based Deep Canonical Correlation Analysis (FR-DCCA), which not only addresses this contradiction but also enables the reconstructing and correlation maximization to benefit from each other. In FR-DCCA, Split Encoder models the information in each view as shared information and specific information; CCA layer maintains consistency through maximizing the canonical correlation between shared information of views; Full Reconstruction module guarantees completeness and complementarity by reconstructing each view with both the shared and specific information. In FR-DCCA, reconstructing and correlation maximization mutually improve each other and yield complete, compact, and discriminative view representations. Experiments and analysis on multiple datasets demonstrate: 1) FR-DCCA significantly outperforms the comparison methods. 2) FR-DCCA effectively addresses the contradiction between reconstruction and correlation maximization. To facilitate future research, we release the codes at https://github.com/FR-DCCA/FR-DCCA.
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Acknowledgement
This work was supported by the NSFC Project under Grant No. 62176069 and 61933013, the Innovation Group of Guangdong Education Department under Grant No. 2020KCXTD014 and the 2019 Key Discipline project of Guangdong Province.
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Sun, Q., Jia, X., Jing, XY. (2022). Addressing Contradiction Between Reconstruction and Correlation Maximization in Deep Canonical Correlation Autoencoders. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13529. Springer, Cham. https://doi.org/10.1007/978-3-031-15919-0_58
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