M$^3$-Impute: Mask-guided Representation Learning for Missing Value Imputation

TL;DR

M$^3$-Impute introduces a novel graph neural network-based method that explicitly leverages missingness information and feature/sample correlations to improve missing value imputation accuracy across diverse datasets.

Contribution

The paper presents M$^3$-Impute, a new imputation approach that models missingness explicitly and captures correlations with novel masking schemes and graph neural networks.

Findings

Achieves 20 best MAE scores out of 25 datasets.

Effectively models missingness and correlations for improved imputation.

Outperforms existing methods on benchmark datasets.

Abstract

Missing values are a common problem that poses significant challenges to data analysis and machine learning. This problem necessitates the development of an effective imputation method to fill in the missing values accurately, thereby enhancing the overall quality and utility of the datasets. Existing imputation methods, however, fall short of explicitly considering the `missingness' information in the data during the embedding initialization stage and modeling the entangled feature and sample correlations during the learning process, thus leading to inferior performance. We propose M$^3$-Impute, which aims to explicitly leverage the missingness information and such correlations with novel masking schemes. M$^3$-Impute first models the data as a bipartite graph and uses a graph neural network to learn node embeddings, where the refined embedding initialization process directly incorporates the missingness information. They are then optimized through M$^3$-Impute's novel feature correlation unit (FRU) and sample correlation unit (SRU) that effectively captures feature and sample correlations for imputation. Experiment results on 25 benchmark datasets under three different missingness settings show the effectiveness of M$^3$-Impute by achieving 20 best and 4 second-best MAE scores on average.