Sparse Online Principal Component for Elliptical Factor Models.
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| Title: | Sparse Online Principal Component for Elliptical Factor Models. |
|---|---|
| Authors: | Zhou, Yuanyuan1 zhouyy0611@163.com, Liu, Siqi2 liusiqi20240130@163.com, Guo, Guangbao3 ggb11111111@163.com |
| Source: | IAENG International Journal of Computer Science. Jul2026, Vol. 53 Issue 7, p2640-2644. 5p. |
| Subjects: | Principal components analysis, Real-time computing, Parameter estimation, Scalability, Sequential learning |
| Abstract: | This paper develops an elliptical factor model that extends traditional factor models to better handle heavytailed data. Parameter estimation employs sparse principal component, which uses penalty terms to induce sparsity in the loading matrix, and a sparse online principal component method for real-time updates. The proposed sparse online principal component framework allows sequential updating of sparse principal components without storing historical data. Simulations confirm the method achieves superior accuracy and sparsity compared to existing approaches. The method is robust and scalable for heavy-tailed datasets. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195088893 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Sparse Online Principal Component for Elliptical Factor Models. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Yuanyuan%22">Zhou, Yuanyuan</searchLink><relatesTo>1</relatesTo><i> zhouyy0611@163.com</i><br /><searchLink fieldCode="AR" term="%22Liu%2C+Siqi%22">Liu, Siqi</searchLink><relatesTo>2</relatesTo><i> liusiqi20240130@163.com</i><br /><searchLink fieldCode="AR" term="%22Guo%2C+Guangbao%22">Guo, Guangbao</searchLink><relatesTo>3</relatesTo><i> ggb11111111@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Jul2026, Vol. 53 Issue 7, p2640-2644. 5p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Principal+components+analysis%22">Principal components analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Real-time+computing%22">Real-time computing</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Scalability%22">Scalability</searchLink><br /><searchLink fieldCode="DE" term="%22Sequential+learning%22">Sequential learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper develops an elliptical factor model that extends traditional factor models to better handle heavytailed data. Parameter estimation employs sparse principal component, which uses penalty terms to induce sparsity in the loading matrix, and a sparse online principal component method for real-time updates. The proposed sparse online principal component framework allows sequential updating of sparse principal components without storing historical data. Simulations confirm the method achieves superior accuracy and sparsity compared to existing approaches. The method is robust and scalable for heavy-tailed datasets. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 5 StartPage: 2640 Subjects: – SubjectFull: Principal components analysis Type: general – SubjectFull: Real-time computing Type: general – SubjectFull: Parameter estimation Type: general – SubjectFull: Scalability Type: general – SubjectFull: Sequential learning Type: general Titles: – TitleFull: Sparse Online Principal Component for Elliptical Factor Models. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhou, Yuanyuan – PersonEntity: Name: NameFull: Liu, Siqi – PersonEntity: Name: NameFull: Guo, Guangbao IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1819656X Numbering: – Type: volume Value: 53 – Type: issue Value: 7 Titles: – TitleFull: IAENG International Journal of Computer Science Type: main |
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