Robust subspace clustering via two-way manifold representation.
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| Title: | Robust subspace clustering via two-way manifold representation. |
|---|---|
| Authors: | Ezeora, Nnamdi Johnson1 (AUTHOR), Anichebe, Gregory Emeka1 (AUTHOR) gregory.anichebe@unn.edu.ng, Nzeh, Royransom Chiemela1,2 (AUTHOR), Uzo, Izuchukwu Uchenna1 (AUTHOR) izuchukwu.uzo@unn.edu.ng |
| Source: | Multimedia Tools & Applications. May2025, Vol. 84 Issue 16, p16339-16356. 18p. |
| Subjects: | Noise, Guilds |
| Abstract: | Subspace clustering has shown great potential in discovering the hidden low-dimensional subspace structures in high-dimensional data. However, most existing methods still face the problem of noise distortion and overlapping subspaces. To tackle this problem and ensure that each sample is only assigned to a single subspace, a new method is proposed in this paper. Specifically, a two-way learning technique is introduced by inducing data manifold via two representative structures. The first is a low-rank structure learned directly from original data. The second structure is an affinity matrix obtained via a k symmetric nearest neighbor graph. By further introducing a dual regularization term, both structures are allowed to guild themselves adaptively to find robust clustering directly without spectral post-processing. In order to evaluate the effectiveness of the proposed method, several experiments are conducted on multiple benchmark datasets. The experimental results, evaluated using six standard metrics, clearly demonstrate that the proposed method outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications 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 |
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| Header | DbId: egs DbLabel: Engineering Source An: 185423665 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-024-19676-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 16339 Subjects: – SubjectFull: Noise Type: general – SubjectFull: Guilds Type: general Titles: – TitleFull: Robust subspace clustering via two-way manifold representation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ezeora, Nnamdi Johnson – PersonEntity: Name: NameFull: Anichebe, Gregory Emeka – PersonEntity: Name: NameFull: Nzeh, Royransom Chiemela – PersonEntity: Name: NameFull: Uzo, Izuchukwu Uchenna IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 84 – Type: issue Value: 16 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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