Hybrid methods improve missing data filling in tables with speed
Hybrid Methods for Robust Tabular Data Imputation
Machine Learning
Summary
Missing information in tables can cause problems in data analysis and machine learning. The authors propose two new methods, NuclearForest and SoftForest, that start by capturing overall patterns in the data using a low-rank approach, then quickly refine missing parts using random forests. These methods work faster than previous popular techniques while keeping or improving accuracy, especially when data are missing in various ways. Their approach handles mixed data types and suits many fields like bioinformatics and economics.
What this means in practice
- •For bioinformatics teams: Fill missing values in biological datasets efficiently to improve downstream analyses and predictions.
- •For economic data analysts: Rapidly complete incomplete economic records to support reliable modeling and forecasting.
Authors
Jinwei Li, Michelle Bruch, Daniel Tenbrinck
Abstract
Missing data are a fundamental challenge in statistical analysis and machine learning, as the choice of imputation method substantially impacts downstream inference. In this work, we propose two hybrid imputation methods called NuclearForest and SoftForest, which combine nuclear-norm-based low-rank initialization using Singular Value Thresholding (SVT) and SoftImpute, respectively, with a non-iterative Random Forest refinement. For the SVT-based component, we further introduce an adaptive step-size rule, prove adaptive step-size bounds, and establish convergence for the corresponding zero-initialized iteration. The low-rank initialization provides a structured warm start that captures the global covariance patterns in the data, while the subsequent Random Forest step recovers residual nonlinear signals encoding local dependencies. We conduct an extensive benchmark on diverse datasets from different application domains, comparing the proposed methods with seven established imputation methods under the Missing Completely at Random (MCAR), Missing at Random (MAR), and Missing Not at Random (MNAR) mechanisms across varying missingness rates. Our results demonstrate that NuclearForest and SoftForest match or exceed the imputation fidelity of state-of-the-art iterative methods such as MissForest, while significantly reducing computational cost. In particular, they achieve speedups of approximately 5.81 times and 9.52 times over MissForest by replacing iterative cycles with a single refinement step. Our approach effectively exploits the low-rank structure of real-world tabular data and accommodates mixed-type variables, providing an efficient and robust solution for data imputation in bioinformatics, economics, and beyond.