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dc.contributor.authorCeylan, Oğuzhan
dc.contributor.authorTaşkın, Gülşen
dc.date.accessioned2020-12-18T21:40:34Z
dc.date.available2020-12-18T21:40:34Z
dc.date.issued2019
dc.identifier.issn2153-6996en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/3578
dc.description.abstractDimensionality reduction has been an active research topic in hyperspectral image analysis due to complexity and non-linearity of the hundreds of the spectral bands. Locality preserving projection (LPP) is a linear extension of the manifold learning and has been very effective in dimensionality reduction compared to linear methods. However, its performance heavily depends on construction of the graph affinity matrix, which has two parameters need to be optimized: k-nearest neighbor parameter and heat kernel parameter. These two parameters might be optimally chosen simply based on a grid search when using only one representative kernel parameter for all the features, but this solution is not feasible when considering a generalized heat kernel in construction the affinity matrix. In this paper, we propose to use heuristic methods, including harmony search (HS) and particle swarm optimization (PSO), in exploring the effects of the heat kernel parameters on embedding quality in terms of classification accuracy. The preliminary results obtained with the experiments on the hyperspectral images showed that HS performs better than PSO, and the heat kernel with multiple parameters achieves better performance than the isotropic kernel with single parameter.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectDimensionality reductionen_US
dc.subjectManifold learningen_US
dc.subjectHarmony searchen_US
dc.subjectParticle swarm optimizationen_US
dc.titleGRAPH OPTIMIZED LOCALITY PRESERVING PROJECTION VIA HEURISTIC OPTIMIZATION ALGORITHMSen_US
dc.typeconferenceObjecten_US
dc.identifier.startpage3065en_US
dc.identifier.endpage3068en_US
dc.relation.journal2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2019)en_US
dc.departmentFakülteler, İşletme Fakültesi, Yönetim Bilişim Sistemleri Bölümüen_US
dc.identifier.wosWOS:000519270603026en_US
dc.institutionauthorCeylan, Oğuzhanen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US


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