Learning and Adaptation to Detect Changes and Anomalies in High-Dimensional Data Article Swipe
The problem of monitoring a datastream and detecting whether the data generating process changes from normal to novel and possibly anomalous conditions has relevant applications in many real scenarios, such as health monitoring and quality inspection of industrial processes. A general approach often adopted in the literature is to learn a model to describe normal data and detect as anomalous those data that do not conform to the learned model. However, several challenges have to be addressed to make this approach effective in real world scenarios, where acquired data are often characterized by high dimension and feature complex structures (such as signals and images). We address this problem from two perspectives corresponding to different modeling assumptions on the data-generating process. At first, we model data as realization of random vectors, as it is customary in the statistical literature. In this settings we focus on the change detection problem, where the goal is to detect whether the datastream permanently departs from normal conditions. We theoretically prove the intrinsic difficulty of this problem when the data dimension increases and propose a novel non-parametric and multivariate change-detection algorithm. In the second part, we focus on data having complex structure and we adopt dictionaries yielding sparse representations to model normal data. We propose novel algorithms to detect anomalies in such datastreams and to adapt the learned model when the process generating normal data changes.
Related Topics
Concepts
Computer science
Focus (optics)
Process (computing)
Dimension (graph theory)
Realization (probability)
Data mining
Parametric statistics
Parametric model
Artificial intelligence
Feature (linguistics)
Machine learning
Mathematics
Physics
Statistics
Philosophy
Pure mathematics
Optics
Linguistics
Operating system
Metadata
- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.1007/978-3-030-32094-2_5
- https://link.springer.com/content/pdf/10.1007%2F978-3-030-32094-2_5.pdf
- OA Status
- hybrid
- Cited By
- 2
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2978714406
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2978714406Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/978-3-030-32094-2_5Digital Object Identifier
- Title
-
Learning and Adaptation to Detect Changes and Anomalies in High-Dimensional DataWork title
- Type
-
book-chapterOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-10-01Full publication date if available
- Authors
-
Diego CarreraList of authors in order
- Landing page
-
https://doi.org/10.1007/978-3-030-32094-2_5Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007%2F978-3-030-32094-2_5.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://link.springer.com/content/pdf/10.1007%2F978-3-030-32094-2_5.pdfDirect OA link when available
- Concepts
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Computer science, Focus (optics), Process (computing), Dimension (graph theory), Realization (probability), Data mining, Parametric statistics, Parametric model, Artificial intelligence, Feature (linguistics), Machine learning, Mathematics, Physics, Statistics, Philosophy, Pure mathematics, Optics, Linguistics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2021: 1Per-year citation counts (last 5 years)
- References (count)
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31Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.random | 129 |
| abstract_inverted_index.second | 188 |
| abstract_inverted_index.sparse | 202 |
| abstract_inverted_index.address | 106 |
| abstract_inverted_index.adopted | 44 |
| abstract_inverted_index.changes | 14 |
| abstract_inverted_index.complex | 98, 195 |
| abstract_inverted_index.conform | 66 |
| abstract_inverted_index.departs | 159 |
| abstract_inverted_index.feature | 97 |
| abstract_inverted_index.general | 41 |
| abstract_inverted_index.learned | 69, 222 |
| abstract_inverted_index.problem | 2, 108, 171 |
| abstract_inverted_index.process | 13, 226 |
| abstract_inverted_index.propose | 178, 209 |
| abstract_inverted_index.quality | 35 |
| abstract_inverted_index.several | 72 |
| abstract_inverted_index.signals | 102 |
| abstract_inverted_index.whether | 9, 155 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 71 |
| abstract_inverted_index.acquired | 88 |
| abstract_inverted_index.approach | 42, 81 |
| abstract_inverted_index.changes. | 230 |
| abstract_inverted_index.describe | 54 |
| abstract_inverted_index.images). | 104 |
| abstract_inverted_index.modeling | 115 |
| abstract_inverted_index.possibly | 20 |
| abstract_inverted_index.problem, | 148 |
| abstract_inverted_index.process. | 120 |
| abstract_inverted_index.relevant | 24 |
| abstract_inverted_index.settings | 141 |
| abstract_inverted_index.vectors, | 130 |
| abstract_inverted_index.yielding | 201 |
| abstract_inverted_index.addressed | 77 |
| abstract_inverted_index.anomalies | 214 |
| abstract_inverted_index.anomalous | 21, 60 |
| abstract_inverted_index.customary | 134 |
| abstract_inverted_index.detecting | 8 |
| abstract_inverted_index.detection | 147 |
| abstract_inverted_index.different | 114 |
| abstract_inverted_index.dimension | 95, 175 |
| abstract_inverted_index.effective | 82 |
| abstract_inverted_index.increases | 176 |
| abstract_inverted_index.intrinsic | 167 |
| abstract_inverted_index.structure | 196 |
| abstract_inverted_index.algorithm. | 185 |
| abstract_inverted_index.algorithms | 211 |
| abstract_inverted_index.challenges | 73 |
| abstract_inverted_index.conditions | 22 |
| abstract_inverted_index.datastream | 6, 157 |
| abstract_inverted_index.difficulty | 168 |
| abstract_inverted_index.generating | 12, 227 |
| abstract_inverted_index.industrial | 38 |
| abstract_inverted_index.inspection | 36 |
| abstract_inverted_index.literature | 47 |
| abstract_inverted_index.monitoring | 4, 33 |
| abstract_inverted_index.processes. | 39 |
| abstract_inverted_index.scenarios, | 29, 86 |
| abstract_inverted_index.structures | 99 |
| abstract_inverted_index.assumptions | 116 |
| abstract_inverted_index.conditions. | 162 |
| abstract_inverted_index.datastreams | 217 |
| abstract_inverted_index.literature. | 138 |
| abstract_inverted_index.permanently | 158 |
| abstract_inverted_index.realization | 127 |
| abstract_inverted_index.statistical | 137 |
| abstract_inverted_index.applications | 25 |
| abstract_inverted_index.dictionaries | 200 |
| abstract_inverted_index.multivariate | 183 |
| abstract_inverted_index.perspectives | 111 |
| abstract_inverted_index.characterized | 92 |
| abstract_inverted_index.corresponding | 112 |
| abstract_inverted_index.theoretically | 164 |
| abstract_inverted_index.non-parametric | 181 |
| abstract_inverted_index.data-generating | 119 |
| abstract_inverted_index.representations | 203 |
| abstract_inverted_index.change-detection | 184 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5068666949 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I93860229 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.47999998927116394 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.56572379 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |