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Shared Dictionary Learning Via Coupled Adaptations for Cross-Domain Classification

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Abstract

Dictionary learning has drawn increasing attention for its impressive performance in obtaining the high-fidelity representations of data and extracting semantics. However, when there exists distribution divergence between source and target data, the representations of target data based on the learned dictionary from source data fail to reveal the intrinsic nature of target tasks, which consequently degrades the target performance severely. To tackle this problem, we propose a Shared Dictionary Learning (SDL) method in this paper. SDL learns a shared dictionary by implementing both geometric and statistical adaptations. SDL utilizes the Nyström method to exploit the geometric relationships between domains. Specifically, SDL uses the Nyström method to construct a variable source graph based on the target graph eigensystem and employs the Nyström approximation error to measure the distance between the variable source graph and the ground truth source graph to formalize the geometric divergence. Thus, a domain-invariant graph can be constructed by minimizing the approximation error and can be used to bridge two domains geometrically. Simultaneously, SDL captures the latent statistical commonality underlying two domains via minimizing the Maximum Mean Discrepancy (MMD) distance between domains. Finally, SDL achieves a shared dictionary and a set of corresponding new representations to handle cross-distribution data classification. Extensive experimental results on several popular datasets demonstrate the superiority of SDL.

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Acknowledgements

This work was supported by the Project supported by the National Natural Science Foundation of China (Grant No.61671480), the Major Scientilic and Technological Projects of CNPC under Grant ZD2019-183-008, and the Open Project Program of the National Laboratory of Pattern Recognition(NLPR)(GrantNo.202000009).

Author information

Author notes

  1. Y. Cai and J. Li: Both authors contributed equally to this research.

Authors and Affiliations

  1. College of Control Science and Engineering, China University of Petroleum (East China), Qingdao, China

    Yuying Cai, Baodi Liu, Honglong Chen & Weifeng Liu

  2. College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, China

    Jinfeng Li

  3. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

    Weijia Cao

  4. Department of Computer and Information Science, University of Macau, Macau, China

    Weijia Cao

  5. Institute of Aerospace Information Applications, Co., Ltd., Beijing, China

    Weijia Cao

  6. The Yangtze Three Gorges Technology and Economy Development Co., Ltd., Beijing, China

    Weijia Cao

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  1. Yuying Cai
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Cai, Y., Li, J., Liu, B. et al. Shared Dictionary Learning Via Coupled Adaptations for Cross-Domain Classification. Neural Process Lett 55, 1869–1888 (2023). https://doi.org/10.1007/s11063-022-10967-7

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