Two-pronged feature reduction in spectral clustering with optimized landmark selection

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dc.authorscopusidBahman Arasteh / 39861139000
dc.authorscopusidAsgarali Bouyer / 35177297800
dc.authorwosidBahman Arasteh / AAN-9555-2021
dc.authorwosidAsgarali Bouyer / IYS-5116-2023
dc.contributor.authorRouhi, Alireza
dc.contributor.authorBouyer, Asgarali
dc.contributor.authorArasteh, Bahman
dc.contributor.authorLiu, Xiaoyang
dc.date.accessioned2025-04-18T08:54:37Z
dc.date.available2025-04-18T08:54:37Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractSpectral clustering is widely employed for clustering data points, particularly for non-linear and non-convex structures in high-dimensional spaces. However, it faces challenges due to the high computational cost of eigen decomposition operations and the performance limitations with high-dimensional data. In this paper, we introduce BVA_LSC, a novel spectral clustering algorithm designed to address these challenges. Firstly, we incorporate an advanced feature reduction stage utilizing Barnes-Hut t-SNE and a deep Variational Autoencoder (VAE) to efficiently reduce the dimensionality of the data, thereby accelerating eigen decomposition. Secondly, we propose an adaptive landmark selection strategy that combines the Grey Wolf Optimizer (GWO) with a novel objective function and K-harmonic means clustering. This strategy dynamically determines an optimal number of landmarks, enhancing the representativeness of the data and reducing the size of the similarity matrix. We assess the performance of our algorithm on various standard datasets, demonstrating its superiority over state-of-the-art methods in terms of accuracy and efficiency.
dc.identifier.citationRouhi, A., Bouyer, A., Arasteh, B., & Liu, X. (2024). Two-pronged feature reduction in spectral clustering with optimized landmark selection. Applied Soft Computing, 161, 111775.
dc.identifier.doi10.1016/j.asoc.2024.111775
dc.identifier.endpage20
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85194105877
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.1016/j.asoc.2024.111775
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6636
dc.identifier.volume161
dc.identifier.wosWOS:001244584200001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBouyer, Asgarali
dc.institutionauthorArasteh, Bahman
dc.institutionauthoridBahman Arasteh / 0000-0001-5202-6315
dc.institutionauthoridAsgarali Bouyer / 0000-0002-4808-2856
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofAPPLIED SOFT COMPUTING Applied soft computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectSpectral Clustering
dc.subjectVariational Autoencoder
dc.subjectLandmark
dc.subjectGrey Wolf Optimization
dc.subjectK-Harmonic Means
dc.subjectUnsupervised Learning
dc.titleTwo-pronged feature reduction in spectral clustering with optimized landmark selection
dc.typeArticle

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