Improving the performance of self-organizing map using reweighted zero-attracting method

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dc.authorscopusidAlaa Ali Hameed / 56338374100
dc.authorwosidAlaa Ali Hameed / ABI-8417-2020
dc.contributor.authorHameed, Alaa Ali
dc.contributor.authorJamil, Akhtar
dc.contributor.authorAlazzawi, Esraa Mohammed
dc.contributor.authorMarquez, Fausto Pedro Garcia
dc.contributor.authorFitriyani, Norma Latif
dc.contributor.authorGu, Yeonghyeon
dc.contributor.authorSyafrudin, Muhammad
dc.date.accessioned2025-04-18T10:32:01Z
dc.date.available2025-04-18T10:32:01Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractIn this paper, we introduce a novel approach to enhance the accuracy and convergence behavior of Self-Organizing Maps (SOM) by incorporating a reweighted zero-attracting term into the loss function. We evaluated two SOM versions: conventional SOM and robust adaptive SOM (RASOM). The enhanced versions, reweighted zero-attracting SOM (RZA-SOM) and reweighted zero-attracting RASOM (RZA-RASOM), include an l1 norm in the error function to add a zero-attractor term, which improves weight coefficient adjustments while preserving topology. The models were assessed for convergence speed and misadjustment under sparsity assumptions of the true coefficient matrix, and their robustness was tested under conditions of increased non-zero taps. Using six different datasets, we compared the performance of RZA-SOM and RZA-RASOM against conventional SOM and RA-SOM in terms of accuracy, quantization error, and topology preservation. Experimental results consistently demonstrated that RZA-SOM and RZA-RASOM surpassed the performance of conventional SOM and RA-SOM. © 2024 The Authors
dc.description.sponsorshipInstitute for Information and Communications Technology Promotion 1711193275 , Institute for Information and Communications Technology Promotion
dc.identifier.citationHameed, A. A., Jamil, A., Alazzawi, E. M., Marquez, F. P. G., Fitriyani, N. L., Gu, Y., & Syafrudin, M. (2024). Improving the performance of self-organizing map using reweighted zero-attracting method. Alexandria Engineering Journal, 106, 743-752.
dc.identifier.doi10.1016/j.aej.2024.08.081
dc.identifier.endpage752
dc.identifier.issn11100168
dc.identifier.scopus2-s2.0-85202737712
dc.identifier.scopusqualityQ1
dc.identifier.startpage743
dc.identifier.urihttp://dx.doi.org/10.1016/j.aej.2024.08.081
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7108
dc.identifier.volume106
dc.identifier.wosWOS:001316544900001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorHameed, Alaa Ali
dc.institutionauthoridAlaa Ali Hameed / 0000-0002-8514-9255
dc.language.isoen
dc.publisherElsevier B.V.
dc.relation.ispartofAlexandria Engineering Journal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectQuantization Error
dc.subjectReweighted Zero-attracting
dc.subjectSelf-organizing Map
dc.subjectTopology Preservation
dc.titleImproving the performance of self-organizing map using reweighted zero-attracting method
dc.typeArticle

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