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Introduction and Applicability of Nonlinear Principal Components Analysis

dc.contributor.author Demir, Yildirim
dc.contributor.author Keskin, Siddik
dc.contributor.author Cavusoglu, Seyda
dc.date.accessioned 2025-05-10T17:08:01Z
dc.date.available 2025-05-10T17:08:01Z
dc.date.issued 2021
dc.department T.C. Van Yüzüncü Yıl Üniversitesi en_US
dc.department-temp [Demir, Yildirim] Van Yuzuncu Yil Univ, Iktisadi & Idari Bilimler Fak, Ekonometri Bolumu, Van, Turkey; [Keskin, Siddik] Van Yuzuncu Yil Univ, Tip Fak, Temel Tip Bilimleri Bolumu, Van, Turkey; [Cavusoglu, Seyda] Van Yuzuncu Yil Univ, Ziraat Fak, Bahce Bitkileri Bolumu, Van, Turkey en_US
dc.description.abstract Nonlinear principal component analysis (NLPCA) is a descriptive dimension reduction method that examines the relationships between variables and displays the results numerically and visually in multivariate datasets that have a linear or nonlinear relationship between them. In this study, it was aimed to present the basic explanatory information about nonlinear principal components analysis (NLPCA) and to emphasize its usability by performing application. In the study, data obtained from 270 samples for 17 continuous variables concerning 3 pepper varieties were evaluated by Principal components analysis (PCA). With the 4 principal components obtained as a result of PCA, being 3 categorical variables Variety, storage time and Application were analyzed by NLPCA. In the analysis made with PCA, approximately 74% of the total variance was explained and in the analysis made with NLPCA, approximately 58% was explained as well. As a result of the analysis; it was observed that there was a strong relationship between PC1 and storage time and variety, and PC3 and PC2 variables, while the relationship between PC4 and application variables and all variables was low. As a result; by examining the linear and nonlinear relationships between the variables in the multivariate datasets, these relationships intended to be presented in an easily interpreted and easily understandable way in two-dimensional space; it was emphasized that NLPCA can be used alone and/or together with other multivariate analysis methods. en_US
dc.description.woscitationindex Emerging Sources Citation Index
dc.identifier.doi 10.18016/ksutarimdoga.vi.770817
dc.identifier.endpage 450 en_US
dc.identifier.issn 2619-9149
dc.identifier.issue 2 en_US
dc.identifier.scopusquality N/A
dc.identifier.startpage 442 en_US
dc.identifier.trdizinid 419286
dc.identifier.uri https://doi.org/10.18016/ksutarimdoga.vi.770817
dc.identifier.uri https://hdl.handle.net/20.500.14720/6952
dc.identifier.volume 24 en_US
dc.identifier.wos WOS:000639677000023
dc.identifier.wosquality N/A
dc.language.iso tr en_US
dc.publisher Kahramanmaras Sutcu Imam Univ Rektorlugu en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Dimension Reduction en_US
dc.subject Optimal Scaling en_US
dc.subject Eigenvalue en_US
dc.subject Sod en_US
dc.subject Mda en_US
dc.title Introduction and Applicability of Nonlinear Principal Components Analysis en_US
dc.type Article en_US

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