Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 52, Issue 11, Pages 12414-12428Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2021.3084464
Keywords
Data models; Manifolds; Training; Adaptation models; Computational modeling; Bayes methods; Sun; Graph learning; multiview learning; representational learning; restricted Boltzmann machines (RBM)
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Funding
- National Natural Science Foundation of China [62076096, 62006076]
- Shanghai Municipal Project [20511100900]
- Open Research Fund of KLATASDS-MOE
- Fundamental Research Funds for the Central Universities
- Shanghai Postdoctoral Excellence Program [2019336]
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The study introduces a novel multiview graph RBM model that simultaneously conducts local structural learning and multiview representation learning, achieving superior performance in multiview classification tasks compared to other state-of-the-art multiview classification algorithms.
Recently, the restricted Boltzmann machine (RBM) has aroused considerable interest in the multiview learning field. Although effectiveness is observed, like many existing multiview learning models, multiview RBM ignores the local manifold structure of multiview data. In this article, we first propose a novel graph RBM model, which preserves the data manifold structure and is amenable to Gibbs sampling. Then, we develop a multiview graph RBM model on the basis of the graph RBM, which performs local structural learning and multiview representation learning simultaneously. The proposed multiview model has the following merits: 1) it preserves the data manifold structure for multiview classification and 2) it performs view-consistent representation learning and view-specific representation learning simultaneously. The experimental results show that the proposed multiview model outperforms other state-of-the-art multiview classification algorithms.
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