Unleashing the Potential of Diffusion Models for Incomplete Data Imputation
<|reference_start|>Unleashing the Potential of Diffusion Models for Incomplete Data Imputation: This paper introduces DiffPuter, an iterative method for missing data imputation that leverages the Expectation-Maximization (EM) algorithm and Diffusion Models. By treating missing data as hidden variables that can be updated during model training, we frame the missing data imputation task as an EM problem. During the M-step, DiffPuter employs a diffusion model to learn the joint distribution of both the observed and currently estimated missing data. In the E-step, DiffPuter re-estimates the missing data based on the conditional probability given the observed data, utilizing the diffusion model learned in the M-step. Starting with an initial imputation, DiffPuter alternates between the M-step and E-step until convergence. Through this iterative process, DiffPuter progressively refines the complete data distribution, yielding increasingly accurate estimations of the missing data. Our theoretical analysis demonstrates that the unconditional training and conditional sampling processes of the diffusion model align precisely with the objectives of the M-step and E-step, respectively. Empirical evaluations across 10 diverse datasets and comparisons with 16 different imputation methods highlight DiffPuter's superior performance. Notably, DiffPuter achieves an average improvement of 8.10% in MAE and 5.64% in RMSE compared to the most competitive existing method.<|reference_end|>
@article{zhang2024unleashing,
    title={Unleashing the Potential of Diffusion Models for Incomplete Data
  Imputation},
    author={Hengrui Zhang, Liancheng Fang, Philip S. Yu},
    journal={arXiv preprint arXiv:2405.20690},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20690},
    primaryClass={cs.LG}
}