RefiDiff: Refinement-Aware Diffusion for Efficient Missing Data Imputation

• URL: http://arxiv.org/abs/2505.14451v1
• Date: Tue, 20 May 2025 14:51:07 GMT
• Title: RefiDiff: Refinement-Aware Diffusion for Efficient Missing Data Imputation
• Authors: Md Atik Ahamed, Qiang Ye, Qiang Cheng,
• Abstract summary: Missing values in high-dimensional, mixed-type datasets pose significant challenges for data imputation.<n>We propose an innovative framework, RefiDiff, combining local machine learning predictions with a novel Mamba-based denoising network.<n>RefiDiff outperforms state-the-art (SOTA) methods across missing-value settings with a 4x faster training time than DDPM-based approaches.
• Score: 13.401822039640297
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Missing values in high-dimensional, mixed-type datasets pose significant challenges for data imputation, particularly under Missing Not At Random (MNAR) mechanisms. Existing methods struggle to integrate local and global data characteristics, limiting performance in MNAR and high-dimensional settings. We propose an innovative framework, RefiDiff, combining local machine learning predictions with a novel Mamba-based denoising network capturing interrelationships among distant features and samples. Our approach leverages pre-refinement for initial warm-up imputations and post-refinement to polish results, enhancing stability and accuracy. By encoding mixed-type data into unified tokens, RefiDiff enables robust imputation without architectural or hyperparameter tuning. RefiDiff outperforms state-of-the-art (SOTA) methods across missing-value settings, excelling in MNAR with a 4x faster training time than SOTA DDPM-based approaches. Extensive evaluations on nine real-world datasets demonstrate its robustness, scalability, and effectiveness in handling complex missingness patterns.

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