A comprehensive review of Principal Component Analysis Article Swipe
PCA (Principal Component Analysis) is a method aiming to reduce the dimensions among data analysis, with various applications in neurosciences, finance, and beyond. Data normalization, covariance matrix decomposition, eigenvalue-driven component selection, and other mathematical underpinnings of PCA will be methodically covered in this article. A comparison with SVD decomposition will also be made due to the similarities between the two methods. Additionally, we will discuss contemporary developments like sparse PCA, kernel PCA, and robust PCA that tackle nonlinearity and sparsity by integrating trends like PCA's integration with deep learning, the variation in applied circumstances, and its use in high-dimensional data presentation. Furthermore, this review will also highlight the inherent limits, such as nonlinearity issues, massive datasets, and data contamination. Throughout investigation, this review serves as a map for the researchers tackling with increasingly complex data environments requiring dimensionality reduction and are not certain with the specific PCA type selected to apply.
Related Topics
Concepts
Principal component analysis
Dimensionality reduction
Kernel principal component analysis
Computer science
Data mining
Sparse PCA
Component (thermodynamics)
Artificial intelligence
Kernel (algebra)
Covariance matrix
Curse of dimensionality
Singular value decomposition
Machine learning
Covariance
Pattern recognition (psychology)
Robust principal component analysis
Nonlinear system
Matrix decomposition
Variation (astronomy)
Kernel method
Sparse matrix
Decomposition
Relation (database)
Data reduction
Data type
Data integration
Mathematics
Data modeling
Exploratory data analysis
Metadata
- Type
- article
- Landing Page
- https://doi.org/10.54097/5mmrkr11
- https://drpress.org/ojs/index.php/ajst/article/download/32396/31708
- OA Status
- diamond
- OpenAlex ID
- https://openalex.org/W7105766768
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7105766768Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.54097/5mmrkr11Digital Object Identifier
- Title
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A comprehensive review of Principal Component AnalysisWork title
- Type
-
articleOpenAlex work type
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2025Year of publication
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2025-11-13Full publication date if available
- Authors
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Yubo YeList of authors in order
- Landing page
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https://doi.org/10.54097/5mmrkr11Publisher landing page
- PDF URL
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https://drpress.org/ojs/index.php/ajst/article/download/32396/31708Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://drpress.org/ojs/index.php/ajst/article/download/32396/31708Direct OA link when available
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Principal component analysis, Dimensionality reduction, Kernel principal component analysis, Computer science, Data mining, Sparse PCA, Component (thermodynamics), Artificial intelligence, Kernel (algebra), Covariance matrix, Curse of dimensionality, Singular value decomposition, Machine learning, Covariance, Pattern recognition (psychology), Robust principal component analysis, Nonlinear system, Matrix decomposition, Variation (astronomy), Kernel method, Sparse matrix, Decomposition, Relation (database), Data reduction, Data type, Data integration, Mathematics, Data modeling, Exploratory data analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.reduction | 138 |
| abstract_inverted_index.requiring | 136 |
| abstract_inverted_index.variation | 90 |
| abstract_inverted_index.(Principal | 1 |
| abstract_inverted_index.Throughout | 119 |
| abstract_inverted_index.comparison | 45 |
| abstract_inverted_index.covariance | 25 |
| abstract_inverted_index.dimensions | 11 |
| abstract_inverted_index.selection, | 30 |
| abstract_inverted_index.integrating | 81 |
| abstract_inverted_index.integration | 85 |
| abstract_inverted_index.researchers | 129 |
| abstract_inverted_index.Furthermore, | 101 |
| abstract_inverted_index.applications | 17 |
| abstract_inverted_index.contemporary | 65 |
| abstract_inverted_index.developments | 66 |
| abstract_inverted_index.environments | 135 |
| abstract_inverted_index.increasingly | 132 |
| abstract_inverted_index.mathematical | 33 |
| abstract_inverted_index.methodically | 39 |
| abstract_inverted_index.nonlinearity | 77, 112 |
| abstract_inverted_index.similarities | 56 |
| abstract_inverted_index.Additionally, | 61 |
| abstract_inverted_index.decomposition | 48 |
| abstract_inverted_index.presentation. | 100 |
| abstract_inverted_index.underpinnings | 34 |
| abstract_inverted_index.circumstances, | 93 |
| abstract_inverted_index.contamination. | 118 |
| abstract_inverted_index.decomposition, | 27 |
| abstract_inverted_index.dimensionality | 137 |
| abstract_inverted_index.investigation, | 120 |
| abstract_inverted_index.neurosciences, | 19 |
| abstract_inverted_index.normalization, | 24 |
| abstract_inverted_index.high-dimensional | 98 |
| abstract_inverted_index.eigenvalue-driven | 28 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile.value | 0.81025082 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |