An Interdisciplinary and Cross-Task Review on Missing Data Imputation
Missing data poses a significant challenge within the realm of data science, impeding effective analysis and decision-making across various fields, such as healthcare, bioinformatics, social sciences, e-commerce, and industrial monitoring. Despite extensive research and the development of numerous imputation methods over the years, the existing literature remains disjointed across disciplines. This fragmentation highlights the urgent need for a comprehensive synthesis that bridges statistical foundations with contemporary machine learning innovations.
This article presents a systematic review that delves into the essential concepts surrounding missing data imputation, including:
- Missingness mechanisms
- Single versus multiple imputation
- Different imputation goals
The review explores the characteristics of missing data across various domains, offering a detailed categorization of imputation methods. These methods range from classical techniques, such as regression and the Expectation-Maximization (EM) algorithm, to modern approaches that include:
- Low-rank and high-rank matrix completion
- Deep learning models, including autoencoders, Generative Adversarial Networks (GANs), diffusion models, and graph neural networks
- Large language models
Particular emphasis is placed on methods designed for complex data types. The review addresses challenges associated with:
- Tensors
- Time series
- Streaming data
- Graph-structured data
- Categorical data
- Multimodal data
Beyond a focus on methodologies, the review also emphasizes the integration of imputation techniques with downstream tasks such as classification, clustering, and anomaly detection. It investigates both sequential pipelines and joint optimization frameworks that enhance the efficacy of imputing missing data.
Furthermore, the review assesses theoretical guarantees related to imputation methods alongside available benchmarking resources and evaluation metrics. This critical evaluation allows researchers and practitioners to understand the strengths and weaknesses of various approaches, contributing to more informed choices in real-world applications.
As the field continues to evolve, the review identifies several pressing challenges and future directions for research. Key areas of focus include:
- Model selection and hyperparameter optimization
- The increasing significance of privacy-preserving imputation techniques, particularly via federated learning
- The quest for generalizable models that can effectively adapt across different domains and data types
In conclusion, this comprehensive review not only synthesizes existing literature but also outlines a roadmap for future research in the field of missing data imputation. By fostering interdisciplinary collaboration and encouraging the integration of advanced methodologies, researchers can enhance the reliability and applicability of imputation techniques, ultimately leading to improved decision-making across various sectors.
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