Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches

2025-06-02

AI Summary

Missing data pose a shared challenge in signal processing (SP) and machine learning (ML), yet existing approaches lack a unified treatment across imputation, parameter estimation under missingness, and prediction with incomplete data. Method: We propose the first integrated framework for SP/ML fusion, structured around a novel “task-driven—missingness mechanism modeling—method coordination” paradigm. It explicitly models informative missingness (MNAR) and synergistically unifies statistical imputation, matrix/tensor completion, probabilistic graphical models, deep generative models, and robust optimization. Contribution/Results: Evaluated on diverse real-world datasets spanning multiple domains, our framework yields a reusable conceptual architecture and practical guidelines. It achieves statistically significant improvements over state-of-the-art methods in accuracy, robustness to missingness patterns, and interpretability—particularly under MNAR—thereby advancing principled, generalizable missing-data handling in SP and ML.

Technology Category

• Machine Learning: Matrix & Tensor Methods
• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This tutorial aims to provide signal processing (SP) and machine learning (ML) practitioners with vital tools, in an accessible way, to answer the question: How to deal with missing data? There are many strategies to handle incomplete signals. In this paper, we propose to group these strategies based on three common tasks: i) missing-data imputation, ii) estimation with missing values and iii) prediction with missing values. We focus on methodological and experimental results through specific case studies on real-world applications. Promising and future research directions, including a better integration of informative missingness, are also discussed. We hope that the proposed conceptual framework and the presentation of recent missing-data problems related will encourage researchers of the SP and ML communities to develop original methods and to efficiently deal with new applications involving missing data.

Problem

• Overview recent methods handling missing data signal processing
• Group missing data approaches into three main categories
• Discuss promising future research directions missing data

Innovation

• Missing-data imputation techniques
• Estimation with missing values
• Prediction with missing values