Reviewing Autoencoders for Missing Data Imputation: Technical Trends, Applications and Outcomes Article Swipe
Ricardo Cardoso Pereira
,
Miriam Seoane Santos
,
Pedro Pereira Rodrigues
,
Pedro Henriques Abreu
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1613/jair.1.12312
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1613/jair.1.12312
Missing data is a problem often found in real-world datasets and it can degrade the performance of most machine learning models. Several deep learning techniques have been used to address this issue, and one of them is the Autoencoder and its Denoising and Variational variants. These models are able to learn a representation of the data with missing values and generate plausible new ones to replace them. This study surveys the use of Autoencoders for the imputation of tabular data and considers 26 works published between 2014 and 2020. The analysis is mainly focused on discussing patterns and recommendations for the architecture, hyperparameters and training settings of the network, while providing a detailed discussion of the results obtained by Autoencoders when compared to other state-of-the-art methods, and of the data contexts where they have been applied. The conclusions include a set of recommendations for the technical settings of the network, and show that Denoising Autoencoders outperform their competitors, particularly the often used statistical methods.
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Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1613/jair.1.12312
- https://www.jair.org/index.php/jair/article/download/12312/26633
- OA Status
- diamond
- Cited By
- 77
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3113310528
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3113310528Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1613/jair.1.12312Digital Object Identifier
- Title
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Reviewing Autoencoders for Missing Data Imputation: Technical Trends, Applications and OutcomesWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-12-14Full publication date if available
- Authors
-
Ricardo Cardoso Pereira, Miriam Seoane Santos, Pedro Pereira Rodrigues, Pedro Henriques AbreuList of authors in order
- Landing page
-
https://doi.org/10.1613/jair.1.12312Publisher landing page
- PDF URL
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https://www.jair.org/index.php/jair/article/download/12312/26633Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://www.jair.org/index.php/jair/article/download/12312/26633Direct OA link when available
- Concepts
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Computer science, Missing data, Imputation (statistics), Artificial intelligence, Autoencoder, Machine learning, Hyperparameter, Feature learning, Representation (politics), Artificial neural network, Unsupervised learning, Data mining, Political science, Politics, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
77Total citation count in OpenAlex
- Citations by year (recent)
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2025: 16, 2024: 30, 2023: 14, 2022: 11, 2021: 6Per-year citation counts (last 5 years)
- References (count)
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60Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.by | 118 |
| abstract_inverted_index.in | 7 |
| abstract_inverted_index.is | 2, 36, 91 |
| abstract_inverted_index.it | 11 |
| abstract_inverted_index.of | 16, 34, 53, 72, 77, 106, 114, 127, 141, 147 |
| abstract_inverted_index.on | 94 |
| abstract_inverted_index.to | 28, 49, 64, 122 |
| abstract_inverted_index.The | 89, 136 |
| abstract_inverted_index.and | 10, 32, 39, 42, 59, 80, 87, 97, 103, 126, 150 |
| abstract_inverted_index.are | 47 |
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| abstract_inverted_index.for | 74, 99, 143 |
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| abstract_inverted_index.set | 140 |
| abstract_inverted_index.the | 14, 37, 54, 70, 75, 100, 107, 115, 128, 144, 148, 159 |
| abstract_inverted_index.use | 71 |
| abstract_inverted_index.2014 | 86 |
| abstract_inverted_index.This | 67 |
| abstract_inverted_index.able | 48 |
| abstract_inverted_index.been | 26, 134 |
| abstract_inverted_index.data | 1, 55, 79, 129 |
| abstract_inverted_index.deep | 22 |
| abstract_inverted_index.have | 25, 133 |
| abstract_inverted_index.most | 17 |
| abstract_inverted_index.ones | 63 |
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| abstract_inverted_index.used | 27, 161 |
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| abstract_inverted_index.with | 56 |
| abstract_inverted_index.2020. | 88 |
| abstract_inverted_index.These | 45 |
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| abstract_inverted_index.learn | 50 |
| abstract_inverted_index.often | 5, 160 |
| abstract_inverted_index.other | 123 |
| abstract_inverted_index.study | 68 |
| abstract_inverted_index.their | 156 |
| abstract_inverted_index.them. | 66 |
| abstract_inverted_index.where | 131 |
| abstract_inverted_index.while | 109 |
| abstract_inverted_index.works | 83 |
| abstract_inverted_index.issue, | 31 |
| abstract_inverted_index.mainly | 92 |
| abstract_inverted_index.models | 46 |
| abstract_inverted_index.values | 58 |
| abstract_inverted_index.Missing | 0 |
| abstract_inverted_index.Several | 21 |
| abstract_inverted_index.address | 29 |
| abstract_inverted_index.between | 85 |
| abstract_inverted_index.degrade | 13 |
| abstract_inverted_index.focused | 93 |
| abstract_inverted_index.include | 138 |
| abstract_inverted_index.machine | 18 |
| abstract_inverted_index.missing | 57 |
| abstract_inverted_index.models. | 20 |
| abstract_inverted_index.problem | 4 |
| abstract_inverted_index.replace | 65 |
| abstract_inverted_index.results | 116 |
| abstract_inverted_index.surveys | 69 |
| abstract_inverted_index.tabular | 78 |
| abstract_inverted_index.analysis | 90 |
| abstract_inverted_index.applied. | 135 |
| abstract_inverted_index.compared | 121 |
| abstract_inverted_index.contexts | 130 |
| abstract_inverted_index.datasets | 9 |
| abstract_inverted_index.detailed | 112 |
| abstract_inverted_index.generate | 60 |
| abstract_inverted_index.learning | 19, 23 |
| abstract_inverted_index.methods, | 125 |
| abstract_inverted_index.methods. | 163 |
| abstract_inverted_index.network, | 108, 149 |
| abstract_inverted_index.obtained | 117 |
| abstract_inverted_index.patterns | 96 |
| abstract_inverted_index.settings | 105, 146 |
| abstract_inverted_index.training | 104 |
| abstract_inverted_index.Denoising | 41, 153 |
| abstract_inverted_index.considers | 81 |
| abstract_inverted_index.plausible | 61 |
| abstract_inverted_index.providing | 110 |
| abstract_inverted_index.published | 84 |
| abstract_inverted_index.technical | 145 |
| abstract_inverted_index.variants. | 44 |
| abstract_inverted_index.discussing | 95 |
| abstract_inverted_index.discussion | 113 |
| abstract_inverted_index.imputation | 76 |
| abstract_inverted_index.outperform | 155 |
| abstract_inverted_index.real-world | 8 |
| abstract_inverted_index.techniques | 24 |
| abstract_inverted_index.Autoencoder | 38 |
| abstract_inverted_index.Variational | 43 |
| abstract_inverted_index.conclusions | 137 |
| abstract_inverted_index.performance | 15 |
| abstract_inverted_index.statistical | 162 |
| abstract_inverted_index.Autoencoders | 73, 119, 154 |
| abstract_inverted_index.competitors, | 157 |
| abstract_inverted_index.particularly | 158 |
| abstract_inverted_index.architecture, | 101 |
| abstract_inverted_index.representation | 52 |
| abstract_inverted_index.hyperparameters | 102 |
| abstract_inverted_index.recommendations | 98, 142 |
| abstract_inverted_index.state-of-the-art | 124 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.95245541 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |