An Exploration of the Application of Principal Component Analysis in Big Data Processing Article Swipe
With the arrival of the significant data era, efficiently processing large-scale multidimensional data has become challenging. As a powerful data dimensionality reduction tool, Principal Component Analysis (PCA) plays a vital role in big data processing, especially in information extraction and data simplification, showing unique advantages. The research aims to simplify the data processing process and improve the data processing efficiency by PCA method. The research method adopts the basic theory of PCA, the improvement of the weighted principal component analysis algorithm, and standardized and homogenized data processing techniques to process large-scale multidimensional data sets. The results show that the data dimensionality is significantly reduced after using PCA, for example, in the Analysis of the earnings quality of listed companies in the e-commerce industry, the cumulative variance contribution rate of the first four principal components extracted by PCA reaches 81.623%, which effectively removes the primary information of the original data. PCA not only reduces the complexity of the data, but also retains a large amount of crucial information, which is a significant application value for the processing of big data, especially in the fields of data compression and pattern recognition.
Related Topics
Concepts
Principal component analysis
Dimensionality reduction
Computer science
Data processing
Data mining
Big data
Curse of dimensionality
Sparse PCA
Data quality
Pattern recognition (psychology)
Information processing
Artificial intelligence
Database
Engineering
Biology
Metric (unit)
Operations management
Neuroscience
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2478/amns-2024-0664
- https://sciendo.com/pdf/10.2478/amns-2024-0664
- OA Status
- gold
- Cited By
- 7
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392308751
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392308751Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.2478/amns-2024-0664Digital Object Identifier
- Title
-
An Exploration of the Application of Principal Component Analysis in Big Data ProcessingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Guo Li, Yi QinList of authors in order
- Landing page
-
https://doi.org/10.2478/amns-2024-0664Publisher landing page
- PDF URL
-
https://sciendo.com/pdf/10.2478/amns-2024-0664Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://sciendo.com/pdf/10.2478/amns-2024-0664Direct OA link when available
- Concepts
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Principal component analysis, Dimensionality reduction, Computer science, Data processing, Data mining, Big data, Curse of dimensionality, Sparse PCA, Data quality, Pattern recognition (psychology), Information processing, Artificial intelligence, Database, Engineering, Biology, Metric (unit), Operations management, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 2Per-year citation counts (last 5 years)
- References (count)
-
19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.era, | 8 |
| abstract_inverted_index.four | 132 |
| abstract_inverted_index.only | 152 |
| abstract_inverted_index.rate | 128 |
| abstract_inverted_index.role | 31 |
| abstract_inverted_index.show | 97 |
| abstract_inverted_index.that | 98 |
| abstract_inverted_index.(PCA) | 27 |
| abstract_inverted_index.after | 105 |
| abstract_inverted_index.basic | 69 |
| abstract_inverted_index.data, | 158, 179 |
| abstract_inverted_index.data. | 149 |
| abstract_inverted_index.first | 131 |
| abstract_inverted_index.large | 163 |
| abstract_inverted_index.plays | 28 |
| abstract_inverted_index.sets. | 94 |
| abstract_inverted_index.tool, | 23 |
| abstract_inverted_index.using | 106 |
| abstract_inverted_index.value | 173 |
| abstract_inverted_index.vital | 30 |
| abstract_inverted_index.which | 140, 168 |
| abstract_inverted_index.adopts | 67 |
| abstract_inverted_index.amount | 164 |
| abstract_inverted_index.become | 15 |
| abstract_inverted_index.fields | 183 |
| abstract_inverted_index.listed | 118 |
| abstract_inverted_index.method | 66 |
| abstract_inverted_index.theory | 70 |
| abstract_inverted_index.unique | 44 |
| abstract_inverted_index.arrival | 3 |
| abstract_inverted_index.crucial | 166 |
| abstract_inverted_index.improve | 56 |
| abstract_inverted_index.method. | 63 |
| abstract_inverted_index.pattern | 188 |
| abstract_inverted_index.primary | 144 |
| abstract_inverted_index.process | 54, 90 |
| abstract_inverted_index.quality | 116 |
| abstract_inverted_index.reaches | 138 |
| abstract_inverted_index.reduced | 104 |
| abstract_inverted_index.reduces | 153 |
| abstract_inverted_index.removes | 142 |
| abstract_inverted_index.results | 96 |
| abstract_inverted_index.retains | 161 |
| abstract_inverted_index.showing | 43 |
| abstract_inverted_index.81.623%, | 139 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Analysis | 26, 112 |
| abstract_inverted_index.analysis | 80 |
| abstract_inverted_index.earnings | 115 |
| abstract_inverted_index.example, | 109 |
| abstract_inverted_index.original | 148 |
| abstract_inverted_index.powerful | 19 |
| abstract_inverted_index.research | 47, 65 |
| abstract_inverted_index.simplify | 50 |
| abstract_inverted_index.variance | 126 |
| abstract_inverted_index.weighted | 77 |
| abstract_inverted_index.Component | 25 |
| abstract_inverted_index.Principal | 24 |
| abstract_inverted_index.companies | 119 |
| abstract_inverted_index.component | 79 |
| abstract_inverted_index.extracted | 135 |
| abstract_inverted_index.industry, | 123 |
| abstract_inverted_index.principal | 78, 133 |
| abstract_inverted_index.reduction | 22 |
| abstract_inverted_index.algorithm, | 81 |
| abstract_inverted_index.complexity | 155 |
| abstract_inverted_index.components | 134 |
| abstract_inverted_index.cumulative | 125 |
| abstract_inverted_index.e-commerce | 122 |
| abstract_inverted_index.efficiency | 60 |
| abstract_inverted_index.especially | 36, 180 |
| abstract_inverted_index.extraction | 39 |
| abstract_inverted_index.processing | 10, 53, 59, 87, 176 |
| abstract_inverted_index.techniques | 88 |
| abstract_inverted_index.advantages. | 45 |
| abstract_inverted_index.application | 172 |
| abstract_inverted_index.compression | 186 |
| abstract_inverted_index.effectively | 141 |
| abstract_inverted_index.efficiently | 9 |
| abstract_inverted_index.homogenized | 85 |
| abstract_inverted_index.improvement | 74 |
| abstract_inverted_index.information | 38, 145 |
| abstract_inverted_index.large-scale | 11, 91 |
| abstract_inverted_index.processing, | 35 |
| abstract_inverted_index.significant | 6, 171 |
| abstract_inverted_index.challenging. | 16 |
| abstract_inverted_index.contribution | 127 |
| abstract_inverted_index.information, | 167 |
| abstract_inverted_index.recognition. | 189 |
| abstract_inverted_index.standardized | 83 |
| abstract_inverted_index.significantly | 103 |
| abstract_inverted_index.dimensionality | 21, 101 |
| abstract_inverted_index.simplification, | 42 |
| abstract_inverted_index.multidimensional | 12, 92 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5034547050 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I4210110718 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5699999928474426 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.925013 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |