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

The goal of this paper is to provide an overview of recent methods for handling missing data in signal processing methods, from their origins to the challenges ahead. Missing data approaches are grouped by three main categories: 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 believe that the proposed conceptual framework and the presentation of the main problems related to missing data will encourage researchers of the signal processing community to develop original methods for handling missing values and to deal with new applications involving missing data in an adequate manner.