Summary of Unleashing the Potential Of Diffusion Models For Incomplete Data Imputation, by Hengrui Zhang et al.
Unleashing the Potential of Diffusion Models for Incomplete Data Imputation
by Hengrui Zhang, Liancheng Fang, Philip S. Yu
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces DiffPuter, a novel iterative method for missing data imputation that leverages the Expectation-Maximization (EM) algorithm and Diffusion Models. The authors frame the missing data imputation task as an EM problem, treating missing data as hidden variables that can be updated during model training. This is achieved by employing a diffusion model to learn the joint distribution of both observed and estimated missing data in the M-step. In the E-step, the authors re-estimate the missing data based on the conditional probability given the observed data, utilizing the learned diffusion model. The iterative process alternates between the M-step and E-step until convergence, progressively refining the complete data distribution and yielding increasingly accurate estimations of the missing data. Compared to 16 different imputation methods across 10 diverse datasets, DiffPuter achieves superior performance with an average improvement of 8.10% in MAE and 5.64% in RMSE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to fill in missing data that’s more accurate than other methods. It uses two types of models: Expectation-Maximization (EM) and Diffusion Models. The authors treat the missing data like a puzzle they can solve by using these models. They use one model to learn how the missing data relates to the data we do have, and then they use that information to make better guesses about what’s missing. This process keeps improving until it gets really good at filling in the blanks. In tests with 10 different datasets and many other methods, this new approach did even better than most of them! |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Mae » Probability
