Academic Journal
An alternating minimization algorithm for sparse convolutive non-negative matrix factorization with ℓ1 -norm.
| Τίτλος: | An alternating minimization algorithm for sparse convolutive non-negative matrix factorization with ℓ1 -norm. |
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| Συγγραφείς: | Zhou, Yijia |
| Πηγή: | Electronic Research Archive; 2025, Vol. 33 Issue 12, p1-25, 25p |
| Θεματικοί όροι: | Speech processing systems, Algorithms, Image analysis, Tikhonov regularization, Iterative methods (Mathematics), Data structures, Matrix decomposition, Signal processing |
| Περίληψη: | Convolutive non-negative matrix factorization has been a dominant analytical technique for deriving interpretable insights from data in speech processing, image analysis, data mining, biomedicine, and other fields. In this paper, a sparse convolutive non-negative matrix factorization model was introduced by incorporating an ℓ 1 regularization on representation matrices. This enhancement not only preserved the inherent characteristics of convolutive non-negative matrix factorization, but also promoted sparse data representation, thereby facilitating more efficient data storage and analysis. An alternating minimization algorithm for the presented model was proposed by integrating the alternating direction method of multipliers with the accelerated iterative shrinkage-thresholding algorithm. In addition, a convergence result was presented that the convergence point of the algorithm necessarily constitutes a stable point of the problem. Experimental results showed that the proposed algorithm yielded sparser solutions for synthetic data designed to simulate sparse representation scenarios, and achieved practical applicability in speech dataset, validating its potential for real-world signal processing tasks. [ABSTRACT FROM AUTHOR] |
| Copyright of Electronic Research Archive is the property of American Institute of Mathematical Sciences 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 190851272 RelevancyScore: 1041 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1040.81262207031 |
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| Items | – Name: Title Label: Title Group: Ti Data: An alternating minimization algorithm for sparse convolutive non-negative matrix factorization with ℓ1 -norm. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhou%2C+Yijia%22">Zhou, Yijia</searchLink> – Name: TitleSource Label: Source Group: Src Data: Electronic Research Archive; 2025, Vol. 33 Issue 12, p1-25, 25p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Speech+processing+systems%22">Speech processing systems</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Tikhonov+regularization%22">Tikhonov regularization</searchLink><br /><searchLink fieldCode="DE" term="%22Iterative+methods+%28Mathematics%29%22">Iterative methods (Mathematics)</searchLink><br /><searchLink fieldCode="DE" term="%22Data+structures%22">Data structures</searchLink><br /><searchLink fieldCode="DE" term="%22Matrix+decomposition%22">Matrix decomposition</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Convolutive non-negative matrix factorization has been a dominant analytical technique for deriving interpretable insights from data in speech processing, image analysis, data mining, biomedicine, and other fields. In this paper, a sparse convolutive non-negative matrix factorization model was introduced by incorporating an ℓ 1 regularization on representation matrices. This enhancement not only preserved the inherent characteristics of convolutive non-negative matrix factorization, but also promoted sparse data representation, thereby facilitating more efficient data storage and analysis. An alternating minimization algorithm for the presented model was proposed by integrating the alternating direction method of multipliers with the accelerated iterative shrinkage-thresholding algorithm. In addition, a convergence result was presented that the convergence point of the algorithm necessarily constitutes a stable point of the problem. Experimental results showed that the proposed algorithm yielded sparser solutions for synthetic data designed to simulate sparse representation scenarios, and achieved practical applicability in speech dataset, validating its potential for real-world signal processing tasks. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Electronic Research Archive is the property of American Institute of Mathematical Sciences 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: 25 StartPage: 1 Subjects: – SubjectFull: Speech processing systems Type: general – SubjectFull: Algorithms Type: general – SubjectFull: Image analysis Type: general – SubjectFull: Tikhonov regularization Type: general – SubjectFull: Iterative methods (Mathematics) Type: general – SubjectFull: Data structures Type: general – SubjectFull: Matrix decomposition Type: general – SubjectFull: Signal processing Type: general Titles: – TitleFull: An alternating minimization algorithm for sparse convolutive non-negative matrix factorization with ℓ1 -norm. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhou, Yijia IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 26881594 Numbering: – Type: volume Value: 33 – Type: issue Value: 12 Titles: – TitleFull: Electronic Research Archive Type: main |
| ResultId | 1 |