FILLER: Feature Imputation via Latent Location Exploration and Retrieval

2026-07-25

AI Summary

This work addresses the challenge of feature imputation in real-world machine learning scenarios where missing observations complicate data completion, particularly due to the tension between scalability and structural consistency. The authors propose a novel imputation mechanism based on iterative exploration in the latent space of a generative model, which actively searches for optimal positions within the two-dimensional latent manifold constructed by G-NeuroDAVIS to accurately fill missing values, accompanied by theoretical convergence guarantees. Evaluated across diverse image datasets and missingness patterns, the method consistently outperforms existing approaches, achieving superior performance in terms of RMSE, PSNR, and SSIM metrics. Furthermore, it significantly enhances downstream classification and clustering tasks, with statistical significance confirmed via Wilcoxon signed-rank tests.

Abstract

In real-world machine learning applications, incomplete observations create a fundamental challenge. Researchers have come up with several ideas to address this crucial problem. However, current models still face challenges in balancing scalability and structural consistency. This study proposes a feature imputation method, called FILLER, that deliberately searches the two-dimensional latent space produced by a generative model and fills the missing values with appropriate entries. The generative model is trained on fully observed data to generate samples from the latent space, and FILLER uses this trained model to impute the values missing in the corrupted test samples. In this study, G-NeuroDAVIS serves the purpose of the generative model. This work also presents a mathematical proof on the convergence of the iterative search. Finally, FILLER has been evaluated on several image datasets under random and structured missingness patterns with varying levels of imputation complexities. In order to justify the efficacy of FILLER, it has been compared against existing state-of-the-art solution strategies in terms of RMSE, PSNR, and SSIM. In addition, Wilcoxon signed-rank test has been carried out to validate statistical significance. Moreover, downstream analyses (classification and clustering) have also established the quality of imputation in terms of standard metrics.

Problem

• feature imputation
• missing data
• incomplete observations
• scalability
• structural consistency

Innovation

• feature imputation
• latent space exploration
• generative model
• missing data
• convergence proof