Least angle sparse principal component analysis for ultrahigh dimensional data Article Swipe
Yifan Xie
,
Tianhui Wang
,
Junyoung Kim
,
Kyungsik Lee
,
Myong K. Jeong
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10479-024-06428-0
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s10479-024-06428-0
Principal component analysis (PCA) has been a widely used technique for dimension reduction while retaining essential information. However, the ordinary PCA lacks interpretability, especially when dealing with large scale data. To address this limitation, sparse PCA (SPCA) has emerged as an interpretable variant of ordinary PCA. However, the ordinary SPCA relies on solving a challenging non-convex discrete optimization problem, which maximizes explained variance while constraining the number of non-zero elements in each principal component. In this paper, we propose an innovative least angle SPCA technique to address the computational complexity associated with SPCA, particularly in ultrahigh dimensional data, by sequentially identifying sparse principal components with minimal angles to their corresponding components extracted through ordinary PCA. This sequential identification enables solving the optimization problem in polynomial time, significantly reducing computational challenges. Despite its efficiency gains, our proposed method also preserves the main attributes of SPCA. Through comprehensive experimental results, we demonstrate advantages of our approach as a viable alternative for dealing with the computational difficulties inherent in ordinary SPCA. Notably, our method emerges as an efficient and effective solution for conducting ultrahigh dimensional data analysis, enabling researchers to extract meaningful insights and streamline data interpretation.
Related Topics
Concepts
Principal component analysis
Interpretability
Sparse PCA
Dimensionality reduction
Dimension (graph theory)
Theory of computation
Computer science
Computational complexity theory
Component (thermodynamics)
Identification (biology)
Convex optimization
Artificial intelligence
Algorithm
Data mining
Pattern recognition (psychology)
Mathematics
Regular polygon
Botany
Physics
Biology
Pure mathematics
Thermodynamics
Geometry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s10479-024-06428-0
- https://link.springer.com/content/pdf/10.1007/s10479-024-06428-0.pdf
- OA Status
- hybrid
- Cited By
- 3
- References
- 49
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405559845
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405559845Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/s10479-024-06428-0Digital Object Identifier
- Title
-
Least angle sparse principal component analysis for ultrahigh dimensional dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-18Full publication date if available
- Authors
-
Yifan Xie, Tianhui Wang, Junyoung Kim, Kyungsik Lee, Myong K. JeongList of authors in order
- Landing page
-
https://doi.org/10.1007/s10479-024-06428-0Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s10479-024-06428-0.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s10479-024-06428-0.pdfDirect OA link when available
- Concepts
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Principal component analysis, Interpretability, Sparse PCA, Dimensionality reduction, Dimension (graph theory), Theory of computation, Computer science, Computational complexity theory, Component (thermodynamics), Identification (biology), Convex optimization, Artificial intelligence, Algorithm, Data mining, Pattern recognition (psychology), Mathematics, Regular polygon, Botany, Physics, Biology, Pure mathematics, Thermodynamics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
-
49Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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