An Optimization Technique for Linear Manifold Learning-Based Dimensionality Reduction: Evaluations on Hyperspectral Images

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dc.authorid110138en_US
dc.contributor.authorYılmaz, Atınç
dc.contributor.authorÖztürk, Ümit
dc.date.accessioned2021-12-21T17:16:54Z
dc.date.available2021-12-21T17:16:54Z
dc.date.issued2021
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractManifold learning tries to find low-dimensional manifolds on high-dimensional data. It is useful to omit redundant data from input. Linear manifold learning algorithms have applicability for out-of-sample data, in which they are fast and practical especially for classification purposes. Locality preserving projection (LPP) and orthogonal locality preserving projection (OLPP) are two known linear manifold learning algorithms. In this study, scatter information of a distance matrix is used to construct a weight matrix with a supervised approach for the LPP and OLPP algorithms to improve classification accuracy rates. Low-dimensional data are classified with SVM and the results of the proposed method are compared with some other important existing linear manifold learning methods. Class-based enhancements and coefficients proposed for the formulization are reported visually. Furthermore, the change on weight matrices, band information, and correlation matrices with p-values are extracted and visualized to understand the effect of the proposed method. Experiments are conducted on hyperspectral imaging (HSI) with two different datasets. According to the experimental results, application of the proposed method with the LPP or OLPP algorithms outperformed traditional LPP, OLPP, neighborhood preserving embedding (NPE) and orthogonal neighborhood preserving embedding (ONPE) algorithms. Furthermore, the analytical findings on visualizations show consistency with obtained classification accuracy enhancements.en_US
dc.identifier.citationAppl. Sci. 2021, 11(19), 9063en_US
dc.identifier.doi10.3390/app11199063
dc.identifier.issn2168-8184
dc.identifier.scopus2-s2.0-85125853867en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.3390/app11199063
dc.identifier.wosWOS:000707129800001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.subjectLinear manifold learningen_US
dc.subjectOut-of-sampleen_US
dc.subjectClassificationen_US
dc.subjectWeight matrixen_US
dc.subjectArtificial intelligenceen_US
dc.titleAn Optimization Technique for Linear Manifold Learning-Based Dimensionality Reduction: Evaluations on Hyperspectral Imagesen_US
dc.typeArticleen_US

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