Hybrid dimensionality reduction techniques based on random projections: a literature review.
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| Title: | Hybrid dimensionality reduction techniques based on random projections: a literature review. |
|---|---|
| Authors: | Adeniran, Usman A.1 (AUTHOR), Sanusi, Ridwan A.1,2 (AUTHOR) ridwan.sanusi@kfupm.edu.sa, Adegoke, Nurudeen A.3 (AUTHOR), Ajadi, Jimoh Olawale4 (AUTHOR) |
| Source: | Knowledge & Information Systems. 6/2/2026, Vol. 68 Issue 1, p1-29. 29p. |
| Subjects: | Random projection method, Dimensional reduction algorithms, Machine learning, Fisher discriminant analysis, Principal components analysis, Scalability |
| Abstract: | Dimensionality reduction (DR) represents a fundamental preprocessing step in contemporary data analysis, particularly for datasets characterized by large feature spaces. While traditional linear techniques such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) have long served as standard approaches in the field, they frequently encounter significant computational and scalability challenges when applied to extremely high-dimensional settings. In response to these limitations, random projection (RP) methods, particularly when integrated with complementary dimensionality reduction techniques or machine learning frameworks, have garnered increasing attention as viable alternatives, owing to their computational efficiency and robust theoretical guarantees for preserving essential data structures. This review systematically examines a range of hybrid DR approaches that leverage RP as a foundational component, elucidating their theoretical underpinnings, associated performance trade-offs, and practical deployment considerations across various application domains. Drawing on recent advances in the literature, we synthesize key insights and identify promising directions for future methodological development in this rapidly evolving field. [ABSTRACT FROM AUTHOR] |
| Copyright of Knowledge & Information Systems is the property of Springer Nature 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 | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 194225663 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10115-026-02741-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 1 Subjects: – SubjectFull: Random projection method Type: general – SubjectFull: Dimensional reduction algorithms Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Fisher discriminant analysis Type: general – SubjectFull: Principal components analysis Type: general – SubjectFull: Scalability Type: general Titles: – TitleFull: Hybrid dimensionality reduction techniques based on random projections: a literature review. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Adeniran, Usman A. – PersonEntity: Name: NameFull: Sanusi, Ridwan A. – PersonEntity: Name: NameFull: Adegoke, Nurudeen A. – PersonEntity: Name: NameFull: Ajadi, Jimoh Olawale IsPartOfRelationships: – BibEntity: Dates: – D: 02 M: 06 Text: 6/2/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02191377 Numbering: – Type: volume Value: 68 – Type: issue Value: 1 Titles: – TitleFull: Knowledge & Information Systems Type: main |
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