Trusted Multi-View Deep Learning with Opinion Aggregation


  • Wei Liu School of Computer Engineering and Science, Shanghai University, Shanghai, China
  • Xiaodong Yue School of Computer Engineering and Science, Shanghai University, Shanghai, China Artificial Intelligence Institute of Shanghai University, Shanghai, China
  • Yufei Chen College of Electronics and Information Engineering, Tongji University, Shanghai, China
  • Thierry Denoeux Université de technologie de Compiègne, CNRS UMR 7253 Heudiasyc, Compiègne, France Shanghai University, UTSEUS, Shanghai, China



Machine Learning (ML)


Multi-view deep learning is performed based on the deep fusion of data from multiple sources, i.e. data with multiple views. However, due to the property differences and inconsistency of data sources, the deep learning results based on the fusion of multi-view data may be uncertain and unreliable. It is required to reduce the uncertainty in data fusion and implement the trusted multi-view deep learning. Aiming at the problem, we revisit the multi-view learning from the perspective of opinion aggregation and thereby devise a trusted multi-view deep learning method. Within this method, we adopt evidence theory to formulate the uncertainty of opinions as learning results from different data sources and measure the uncertainty of opinion aggregation as multi-view learning results through evidence accumulation. We prove that accumulating the evidences from multiple data views will decrease the uncertainty in multi-view deep learning and facilitate to achieve the trusted learning results. Experiments on various kinds of multi-view datasets verify the reliability and robustness of the proposed multi-view deep learning method.




How to Cite

Liu, W., Yue, X., Chen, Y., & Denoeux, T. (2022). Trusted Multi-View Deep Learning with Opinion Aggregation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7), 7585-7593.



AAAI Technical Track on Machine Learning II