Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data Article Swipe
Hyeongmin Cho
,
Sangkyun Lee
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/app11020472
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/app11020472
Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning success is the availability of large training data, many datasets are being disclosed and published online. From a data consumer or manager point of view, measuring data quality is an important first step in the learning process. We need to determine which datasets to use, update, and maintain. However, not many practical ways to measure data quality are available today, especially when it comes to large-scale high-dimensional data, such as images and videos. This paper proposes two data quality measures that can compute class separability and in-class variability, the two important aspects of data quality, for a given dataset. Classical data quality measures tend to focus only on class separability; however, we suggest that in-class variability is another important data quality factor. We provide efficient algorithms to compute our quality measures based on random projections and bootstrapping with statistical benefits on large-scale high-dimensional data. In experiments, we show that our measures are compatible with classical measures on small-scale data and can be computed much more efficiently on large-scale high-dimensional datasets.
Related Topics
Concepts
Computer science
Bootstrapping (finance)
Scale (ratio)
Data mining
Class (philosophy)
Data quality
Machine learning
Quality (philosophy)
Artificial intelligence
Key (lock)
Algorithm
Mathematics
Computer security
Epistemology
Philosophy
Operations management
Quantum mechanics
Metric (unit)
Physics
Economics
Econometrics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app11020472
- https://www.mdpi.com/2076-3417/11/2/472/pdf?version=1609924780
- OA Status
- gold
- Cited By
- 4
- References
- 26
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3118765387
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3118765387Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app11020472Digital Object Identifier
- Title
-
Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-06Full publication date if available
- Authors
-
Hyeongmin Cho, Sangkyun LeeList of authors in order
- Landing page
-
https://doi.org/10.3390/app11020472Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/11/2/472/pdf?version=1609924780Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-3417/11/2/472/pdf?version=1609924780Direct OA link when available
- Concepts
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Computer science, Bootstrapping (finance), Scale (ratio), Data mining, Class (philosophy), Data quality, Machine learning, Quality (philosophy), Artificial intelligence, Key (lock), Algorithm, Mathematics, Computer security, Epistemology, Philosophy, Operations management, Quantum mechanics, Metric (unit), Physics, Economics, EconometricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
26Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.landing_page_url | https://doi.org/10.3390/app11020472 |
| publication_date | 2021-01-06 |
| publication_year | 2021 |
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