Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911354417315840 |
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| author | Hippert-Ferrer, Alexandre Sportisse, Aude Javaheri, Amirhossein Korso, Mohammed Nabil El Palomar, Daniel P. |
| author_facet | Hippert-Ferrer, Alexandre Sportisse, Aude Javaheri, Amirhossein Korso, Mohammed Nabil El Palomar, Daniel P. |
| contents | 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 analytical 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 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01696 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches Hippert-Ferrer, Alexandre Sportisse, Aude Javaheri, Amirhossein Korso, Mohammed Nabil El Palomar, Daniel P. Signal Processing Machine Learning 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 analytical 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 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. |
| title | Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2506.01696 |