An Adaptive Affinity Matrix Optimization for Locality Preserving Projection via Heuristic Methods for Hyperspectral Image Analysis

dc.contributor.authorCeylan, Oğuzhan
dc.contributor.authorCeylan, Oğuzhan
dc.date.accessioned2020-12-12T09:22:46Z
dc.date.available2020-12-12T09:22:46Z
dc.date.issued2019
dc.departmentFakülteler, İşletme Fakültesi, Yönetim Bilişim Sistemleri Bölümüen_US
dc.description.abstractLocality preserving projection (LPP) has been often used as a dimensionality reduction tool for hyperspectral image analysis especially in the context of classification since it provides a projection matrix for embedding test samples to low dimensional space. However, the performance of LPP heavily depends on the optimization of two parameters of the graph affinity matrix: k-nearest neighbor and heat kernel width, when one considers an isotropic kernel. These two parameters might be optimally chosen simply based on a grid search. In case of using a generalized heat kernel where each feature is separately weighted by a kernel width, the number of parameters that need to be optimized is related to the number of features of the dataset, which might not be very easy to tune. Therefore, in this article, we propose to use heuristic methods, including genetic algorithm (GA), harmony search (HS), and particle swarm optimization (PSO), to explore the effects of the heat kernel parameters aiming to analyze the embedding quality of LPP's projection in terms of various aspects, including 1-NN classification accuracy, locality preserving power, and quality of the graph affinity matrix. The results obtained with the experiments on three hyperspectral datasets show that HS performs better than GA and PSO in optimizing the parameters of the affinity matrix, and the generalized heat kernel achieves better performance than the isotropic kernel. Additionally, a feature selection application is performed by using the kernel width of the generalized heat kernel for each heuristic method. The results show that very promising results are obtained in comparison with the state-of-the-art feature selection methods.en_US
dc.description.sponsorshipTubitaken_US
dc.identifier.citation4
dc.identifier.doi10.1109/JSTARS.2019.2947355en_US
dc.identifier.endpage4697en_US
dc.identifier.issn1939-1404en_US
dc.identifier.issn2151-1535en_US
dc.identifier.issn1939-1404
dc.identifier.issn2151-1535
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85079348756en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage4690en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/3518
dc.identifier.urihttps://doi.org/10.1109/JSTARS.2019.2947355
dc.identifier.volume12en_US
dc.identifier.wosWOS:000515698700001en_US
dc.identifier.wosqualityQ2
dc.institutionauthorCeylan, Oğuzhanen_US
dc.language.isoenen_US
dc.publisherIEEE-Inst Electrıcal Electronıcs Engıneers Incen_US
dc.relation.journalIEEE Journal of Selected Topıcs in Applıed Earth Observatıons and Remote Sensıngen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDimensionality reduction'en_US
dc.subjectGenetic algorithmsen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectFeature extractionen_US
dc.subjectOptimizationen_US
dc.subjectDimensionality reductionen_US
dc.subjectGenetic algorithmen_US
dc.subjectHarmony searchen_US
dc.subjectManifold learningen_US
dc.subjectParticle swarm optimizationen_US
dc.titleAn Adaptive Affinity Matrix Optimization for Locality Preserving Projection via Heuristic Methods for Hyperspectral Image Analysisen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublicationb80c3194-906c-4e78-a54c-e3cd1effc970
relation.isAuthorOfPublication.latestForDiscoveryb80c3194-906c-4e78-a54c-e3cd1effc970

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